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Pentaho 3.2 Data Integration
Beginner's Guide
Explore, transform, validate, and integrate your data with ease
María Carina Roldán
Pentaho 3.2 Data Integration
Beginner's Guide
Copyright © 2010 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system,
or transmied in any form or by any means, without the prior wrien permission of the
publisher, except in the case of brief quotaons embedded in crical arcles or reviews.
Every eort has been made in the preparaon of this book to ensure the accuracy of the
informaon presented. However, the informaon contained in this book is sold without
warranty, either express or implied. Neither the author, Packt Publishing, nor its dealers or
distributors will be held liable for any damages caused or alleged to be caused directly or
indirectly by this book.
Packt Publishing has endeavored to provide trademark informaon about all the companies
and products menoned in this book by the appropriate use of capitals. However, Packt
Publishing cannot guarantee the accuracy of this informaon.
First published: April 2010
Producon Reference: 1050410
Published by Packt Publishing Ltd.
32 Lincoln Road
Birmingham, B27 6PA, UK.
ISBN 978-1-847199-54-6
Cover Image by Parag Kadam (
María Carina Roldán
Jens Bleuel
Roland Bouman
Ma Casters
James Dixon
Will Gorman
Gretchen Moran
Acquision Editor
Usha Iyer
Development Editor
Reshma Sundaresan
Technical Editors
Gaurav Datar
Rukhsana Khambaa
Copy Editor
Sanchari Mukherjee
Editorial Team Leader
Gagandeep Singh
Project Team Leader
Lata Basantani
Project Coordinator
Poorvi Nair
Sandra Hopper
Rekha Nair
Geetanjali Sawant
Producon Coordinator
Shantanu Zagade
Cover Work
Shantanu Zagade
If we look back at what has happened in the data integraon market over the last 10
years we can see a lot of change. In the rst half of that decade there was an explosion
in the number of data integraon tools and in the second half there was a big wave of
consolidaons. This consolidaon wave put an ever growing amount of data integraon
power in the hands of only a few large billion dollar companies. For any person, company
or project in need of data integraon, this meant either paying large amounts of money or
doing hand-coding of their soluon.
During that exact same period, we saw web servers, programming languages, operang
systems, and even relaonal databases turn into a commodity in the ICT market place. This
was driven among other things by the availability of open source soware such as Apache,
GNU, Linux, MySQL, and many others. For the ICT market, this meant that more services
could be deployed at a lower cost. If you look closely at what has been going on in those last
10 years, you will noce that most companies increasingly deployed more ICT services to
end-users. These services get more and more connected over an ever growing network.
Prey much anything ranging from ny mobile devices to huge cloud-based infrastructure
is being deployed and all those can contain data that is valuable to an organizaon.
The job of any person that needs to integrate all this data is not easy. Complexity of
informaon services technology usually increases exponenally with the number of systems
involved. Because of this, integrang all these systems can be a daunng and scary task that
is never complete. Any piece of code lives in what can be described as a soware ecosystem
that is always in a state of ux. Like in nature, certain ecosystems evolve extremely fast
where others change very slowly over me. However, like in nature all ICT systems change.
What is needed is another wave of commodicaon in the area of data integraon and
business intelligence in general. This is where Pentaho comes in.
Pentaho tries to provide answers to these problems by making the integraon soware
available as open source, accessible, easy to use, and easy to maintain for users and
developers alike. Every release of our soware we try to make things easier, beer, and
faster. However, even if things can be done with nice user interfaces, there are sll a huge
amount of possibilies and opons to choose from.
As the founder of the project I've always liked the fact that Kele users had a lot of choice.
Choice translates into creavity, and creavity oen delivers good soluons that are
comfortable to the person implemenng them. However, this choice can be daunng to any
beginning Kele developer. With thousands of opons to choose from, it can be very hard to
get started.
This is above all others the reason why I'm very happy to see this book come to life. It will
be a great and indispensable help for everyone that is taking steps into the wonderful world
of data integraon with Kele. As such, I hope you see this book as an open invitaon to get
started with Kele in the wonderful world of data integraon.
Ma Casters
Chief Data Integraon at Pentaho
Kele founder
The Kettle Project
Whether there is a migraon to do, an ETL process to run, or a need for massively loading
data into a database, you have several soware tools, ranging from expensive and
sophiscated to free open source and friendly ones, which help you accomplish the task.
Ten years ago, the scenario was clearly dierent. By 2000, Ma Casters, a Belgian business
intelligent consultant, had been working for a while as a datawarehouse architect and
administrator. As such, he was one of quite a number of people who, no maer if the
company they worked for was big or small, had to deal with the dicules that involve
bridging the gap between informaon technology and business needs. What made it even
worse at that me was that ETL tools were prohibively expensive and everything had to
be craed done. The last employer he worked for, didn't think that wring a new ETL tool
would be a good idea. This was one of the movaons for Ma to become an independent
contractor and to start his own company. That was in June 2001.
At the end of that year, he told his wife that he was going to write a new piece of soware
for himself to do ETL tasks. It was going to take up some me le and right in the evenings
and weekends. Surprised, she asked how long it would take you to get it done. He replied
that it would probably take ve years and that he perhaps would have something working
in three.
Working on that started in early 2003. Ma's main goals for wring the soware included
learning about databases, ETL processes, and data warehousing. This would in turn improve
his chances on a job market that was prey volale. Ulmately, it would allow him to work
full me on the soware.
Another important goal was to understand what the tool had to do. Ma wanted a scalable
and parallel tool, and wanted to isolate rows of data as much as possible.
The last but not least goal was to pick the right technology that would support the tool. The
rst idea was to build it on top of KDE, the popular Unix desktop environment. Trolltech, the
people behind Qt, the core UI library of KDE, had released database plans to create drivers
for popular databases. However, the lack of decent drivers for those databases drove Ma
to change plans and use Java. He picked Java because he had some prior experience as he
had wrien a Japanese Chess (Shogi) database program when Java 1.0 was released. To
Sun's credit, this soware sll runs and is available at
Aer a year of development, the tool was capable of reading text les, reading from
databases, wring to databases and it was very exible. The experience with Java was not
100% posive though. The code had grown unstructured, crashes occurred all too oen, and
it was hard to get something going with the Java graphic library used at that moment, the
Abstract Window Toolkit (AWT); it looked bad and it was slow.
As for the library, Ma decided to start using the newly released Standard Widget Toolkit
(SWT), which helped solve part of the problem. As for the rest, Kele was a complete mess.
It was me to ask for help. The help came in hands of Wim De Clercq, a senior enterprise
Java architect, co-owner of Ixor ( and also friend of Ma. At various intervals
over the next few years, Wim involved himself in the project, giving advices to Ma about
good pracces in Java programming. Listening to that advice meant performing massive
amounts of code changes. As a consequence, it was not unusual to spend weekends doing
nothing but refactoring code and xing thousands of errors because of that. But, bit by bit,
things kept going in the right direcon.
At that same me, Ma also showed the results to his peers, colleagues, and other senior
BI consultants to hear what they thought of Kele. That was how he got in touch with the
Flemish Trac Centre ( where billions
of rows of data had to be integrated from thousands of data sources all over Belgium. All of
a sudden, he was being paid to deploy and improve Kele to handle that job. The diversity of
test cases at the trac center helped to improve Kele dramacally. That was somewhere in
2004 and Kele was by its version 1.2.
While working at Flemish, Ma also posted messages on Javaforge (
to let people know they could download a free copy of Kele for their own use. He got a
few reacons. Despite some of them being remarkably negave, most were posive. The
most interesng response came from a nice guy called Jens Bleuel in Germany who asked if
it was possible to integrate third-party soware into Kele. In his specic case, he needed a
connector to link Kele with the German SAP soware ( Kele didn't have a
plugin architecture, so Jens' queson made Ma think about a plugin system, and that was
the main movaon for developing version 2.0.
For various reasons including the birth of Ma's son Sam and a lot of consultancy work,
it took around a year to release Kele version 2.0. It was a fairly complete release with
advanced support for slowly changing dimensions and junk dimensions (Chapter 9 explains
those concepts), ability to connect to thirteen dierent databases, and the most important
fact being support for plugins. Ma contacted Jens to let him know the news and Jens was
really interested. It was a very memorable moment for Ma and Jens as it took them only a
few hours to get a new plugin going that read data from an SAP/R3 server. There was a lot
of excitement, and they agreed to start promong the sales of Kele from the
website and from Prorao (, the company Jens worked for.
Those were days of improvements, requests, people interested in the project. However, it
became too much to handle. Doing development and sales all by themselves was no fun
aer a while. As such, Ma thought about open sourcing Kele early in 2005 and by late
summer he made his decision. Jens and Prorao didn't mind and the decision was nal.
When they nally open sourced Kele on December 2005, the response was massive. The
downloadable package put up on Javaforge got downloaded around 35000 mes during rst
week only. The news got spread all over the world prey quickly.
What followed was a ood of messages, both private and on the forum. At its peak in March
2006, Ma got over 300 messages a day concerning Kele.
In no me, he was answering quesons like crazy, allowing people to join the development
team and working as a consultant at the same me. Added to this, the birth of his daughter
Hannelore in February 2006 was too much to deal with.
Fortunately, good mes came. While Ma was trying to handle all that, a discussion was
taking place at the Pentaho forum ( concerning the ETL
tool that Pentaho should support. They had selected Enhydra Octopus, a Java-based ETL
soware, but they didn't have a strong reliance on a specic tool.
While Jens was evaluang all sorts of open source BI packages, he came across that thread.
Ma replied immediately persuading people at Pentaho to consider including Kele. And
he must be convincing because the answer came quickly and was posive. James Dixon,
Pentaho founder and CTO, opened Kele the possibility to be the premier and only ETL
tool supported by Pentaho. Later on, Ma came in touch with one of the other Pentaho
founders, Richard Daley, who oered him a job. That allowed Ma to focus full-me on
Kele. Four years later, he's sll happily working for Pentaho as chief architect for data
integraon, doing the best eort to deliver Kele 4.0. Jens Bleuel, who collaborated with
Ma since the early versions, is now also part of the Pentaho team.
About the Author
María Carina was born in a small town in the Patagonia region in Argenna. She earned
her Bachelor degree in Computer Science at UNLP in La Plata and then moved to Buenos
Aires where she has lived since 1994 working in IT.
She has been working as a BI consultant for the last 10 years. At the beginning she worked
with Cognos suite. However, over the last three years, she has been dedicated, full me, to
developing Pentaho BI soluons both for local and several Lan-American companies, as well
as for a French automove company in the last months.
She is also an acve contributor to the Pentaho community.
At present, she lives in Buenos Aires, Argenna, with her husband Adrián and children
Camila and Nicolás.
Wring my rst book in a foreign language and working on a full me job
at the same me, not to menon the upbringing of two small kids, was
denitely a big challenge. Now I can tell that it's not impossible.
I dedicate this book to my husband and kids; I'd like to thank them for all
their support and tolerance over the last year. I'd also like to thank my
colleagues and friends who gave me encouraging words throughout the
wring process.
Special thanks to the people at Packt; working with them has been
really pleasant.
I'd also like to thank the Pentaho community and developers for making
Kele the incredible tool it is. Thanks to the technical reviewers who,
with their very crical eye, contributed to make this a book suited to
the audience.
Finally, I'd like to thank Ma Casters who, despite his busy schedule, was
willing to help me from the rst moment he knew about this book.
About the Reviewers
Jens Bleuel is a Senior Consultant and Engineer at Pentaho. He is also working as a project
leader, trainer, and product specialist in the services and support department. Before he
joined Pentaho in mid 2007, he was soware developer and project leader, and his main
business was Data Warehousing and the architecture along with designing and developing of
user friendly tools. He studied business economics, was on a grammar school for electronics,
and has been programming in a wide area of environments such as Assembler, C, Visual
Basic, Delphi, .NET, and these days mainly in Java. His customer focus is on the wholesale
market and consumer goods industries. Jens is 40 years old and lives with his wife and two
boys in Mainz, Germany (near the nice Rhine river). In his spare me, he pracces Tai-Chi,
Qigong, and photography.
Roland Bouman has been working in the IT industry since 1998, mostly as a database and
web applicaon developer. He has also worked for MySQL AB (later Sun Microsystems) as
cercaon developer and as curriculum developer.
Roland mainly focuses on open source web technology, databases, and Business Intelligence.
He's an acve member of the MySQL and Pentaho communies and can oen be found
speaking at worldwide conferences and events such as the MySQL user conference, the
O'Reilly Open Source conference (OSCON), and at Pentaho community events.
Roland is co-author of the MySQL 5.1 Cluster DBA Cercaon Study Guide (Vervante,
ISBN: 595352502) and Pentaho Soluons: Business Intelligence and Data Warehousing with
Pentaho and MySQL (Wiley, ISBN: 978-0-470-48432-6). He also writes on a regular basis for
the Dutch Database Magazine (DBM).
Roland is @rolandbouman on Twier and maintains a blog at
Ma Casters has been an independent senior BI consultant for almost two decades. In that
period he led, designed, and implemented numerous data warehouses and BI soluons for
large and small companies. In that capacity, he always had the need for ETL in some form
or another. Almost out of pure necessity, he has been busy wring the ETL tool called Kele
(a.k.a. Pentaho Data Integraon) for the past eight years. First, he developed the tool mostly
on his own. Since the end of 2005 when Kele was declared an open source technology,
development took place with the help of a large community.
Since the Kele project was acquired by Pentaho in early 2006, he has been Chief of Data
Integraon at Pentaho as the lead architect, head of development, and spokesperson for the
Kele community.
I would like to personally thank the complete community for their help
in making Kele the success it is today. In parcular, I would like to thank
Maria for taking the me to write this nice book as well as the many
arcles on the Pentaho wiki (for example, the Kele tutorials), and her
appreciated parcipaon on the forum. Many thanks also go to my
employer Pentaho, for their large investment in open source BI in
general and Kele in parcular.
James Dixon is the Chief Geek and one of the co-founders of Pentaho Corporaon—the
leading commercial open source Business Intelligence company. He has worked in the
business intelligence market since graduang in 1992 from Southampton University with a
degree in Computer Science. He has served as Soware Engineer, Development Manager,
Engineering VP, and CTO at mulple business intelligence soware companies. He regularly
uses Pentaho Data Integraon for internal projects and was involved in the architectural
design of PDI V3.0.
He lives in Orlando, Florida, with his wife Tami and son Samuel.
I would like to thank my co-founders, my parents, and my wife Tami for all
their support and tolerance of my odd working hours.
I would like to thank my son Samuel for all the opportunies he gives me to
prove I'm not as clever as I think I am.
Will Gorman is an Engineering Team Lead at Pentaho. He works on a variety of Pentaho's
products, including Reporng, Analysis, Dashboards, Metadata, and the BI Server. Will
started his career at GE Research and earned his Masters degree in Computer Science at
Rensselaer Polytechnic Instute in Troy, New York. Will is the author of Pentaho Reporng
3.5 for Java Developers (ISBN: 3193), published by Packt Publishing.
Gretchen Moran is a graduate of University of Wisconsin – Stevens Point with a Bachelor's
degree in Computer Informaon Systems with a minor in Data Communicaons. Gretchen
began her career as a corporate data warehouse developer in the insurance industry and
joined Arbor Soware/Hyperion Soluons in 1999 as a commercial developer for the
Hyperion Analyzer and Web Analycs team. Gretchen has been a key player with Pentaho
Corporaon since its incepon in 2004. As Community Leader and core developer, Gretchen
managed the explosive growth of Pentaho's open source community for her rst 2 years
with the company. Gretchen has contributed to many of the Pentaho projects, including the
Pentaho BI Server, Pentaho Data Integraon, Pentaho Metadata Editor, Pentaho Reporng,
Pentaho Charng, and others.
Thanks Doug, Anthony, Isabella and Baby Jack for giving me my favorite
challenges and crowning achievements—being a wife and mom.
Table of Contents
Preface 1
Chapter 1: Geng started with Pentaho Data Integraon 7
Pentaho Data Integraon and Pentaho BI Suite 7
Exploring the Pentaho Demo 9
Pentaho Data Integraon 9
Using PDI in real world scenarios 11
Loading data warehouses or data marts 11
Integrang data 12
Data cleansing 12
Migrang informaon 13
Exporng data 13
Integrang PDI using Pentaho BI 13
Installing PDI 14
Time for acon – installing PDI 14
Launching the PDI graphical designer: Spoon 15
Time for acon – starng and customizing Spoon 15
Spoon 18
Seng preferences in the Opons window 18
Storing transformaons and jobs in a repository 19
Creang your rst transformaon 20
Time for acon – creang a hello world transformaon 20
Direcng the Kele engine with transformaons 25
Exploring the Spoon interface 26
Running and previewing the transformaon 27
Time for acon – running and previewing the
hello_world transformaon 27
Installing MySQL 29
Time for acon – installing MySQL on Windows 29
Time for acon – installing MySQL on Ubuntu 32
Summary 34
Table of Contents
[ ii ]
Chapter 2: Geng Started with Transformaons 35
Reading data from les 35
Time for acon – reading results of football matches from les 36
Input les 41
Input steps 41
Reading several les at once 42
Time for acon – reading all your les at a me using a single
Text le input step 42
Time for acon – reading all your les at a me using a single
Text le input step and regular expressions 43
Regular expressions 44
Grids 46
Sending data to les 47
Time for acon – sending the results of matches to a plain le 47
Output les 49
Output steps 50
Some data denions 50
Rowset 50
Streams 51
The Select values step 52
Geng system informaon 52
Time for acon – updang a le with news about examinaons 53
Geng informaon by using Get System Info step 57
Data types 58
Date elds 58
Numeric elds 59
Running transformaons from a terminal window 60
Time for acon – running the examinaon transformaon from
a terminal window 60
XML les 62
Time for acon – geng data from an XML le with informaon
about countries 62
What is XML 67
PDI transformaon les 68
Geng data from XML les 68
XPath 68
Conguring the Get data from XML step 69
Kele variables 70
How and when you can use variables 70
Summary 72
Table of Contents
[ iii ]
Chapter 3: Basic data manipulaon 73
Basic calculaons 73
Time for acon – reviewing examinaons by using the Calculator step 74
Adding or modifying elds by using dierent PDI steps 82
The Calculator step 83
The Formula step 84
Time for acon – reviewing examinaons by using the Formula step 84
Calculaons on groups of rows 88
Time for acon – calculang World Cup stascs by grouping data 89
Group by step 94
Filtering 97
Time for acon – counng frequent words by ltering 97
Filtering rows using the Filter rows step 103
Looking up data 105
Time for acon – nding out which language people speak 105
The Stream lookup step 109
Summary 112
Chapter 4: Controlling the Flow of Data 113
Spling streams 113
Time for acon – browsing new PDI features by copying a dataset 114
Copying rows 119
Distribung rows 120
Time for acon – assigning tasks by distribung 121
Spling the stream based on condions 125
Time for acon – assigning tasks by ltering priories with the Filter rows step 126
PDI steps for spling the stream based on condions 128
Time for acon – assigning tasks by ltering priories with the Switch/ Case step 129
Merging streams 131
Time for acon – gathering progress and merging all together 132
PDI opons for merging streams 134
Time for acon – giving priority to Bouchard by using Append Stream 137
Summary 139
Chapter 5: Transforming Your Data with JavaScript Code and
the JavaScript Step 141
Doing simple tasks with the JavaScript step 141
Time for acon – calculang scores with JavaScript 142
Using the JavaScript language in PDI 147
Inserng JavaScript code using the Modied Java Script Value step 148
Adding elds 150
Table of Contents
[ iv ]
Modifying elds 150
Turning on the compability switch 151
Tesng your code 151
Time for acon – tesng the calculaon of averages 152
Tesng the script using the Test script buon 153
Enriching the code 154
Time for acon – calculang exible scores by using variables 154
Using named parameters 158
Using the special Start, Main, and End scripts 159
Using transformaon predened constants 159
Reading and parsing unstructured les 162
Time for acon – changing a list of house descripons with JavaScript 162
Looking at previous rows 164
Avoiding coding by using purpose-built steps 165
Summary 167
Chapter 6: Transforming the Row Set 169
Converng rows to columns 169
Time for acon – enhancing a lms le by converng rows to columns 170
Converng row data to column data by using the Row denormalizer step 173
Aggregang data with a Row denormalizer step 176
Time for acon – calculang total scores by performances by country 177
Using Row denormalizer for aggregang data 178
Normalizing data 180
Time for acon – enhancing the matches le by normalizing the dataset 180
Modifying the dataset with a Row Normalizer step 182
Summarizing the PDI steps that operate on sets of rows 184
Generang a custom me dimension dataset by using Kele variables 186
Time for acon – creang the me dimension dataset 187
Geng variables 191
Time for acon – geng variables for seng the default starng date 192
Using the Get Variables step 193
Summary 194
Chapter 7: Validang Data and Handling Errors 195
Capturing errors 195
Time for acon – capturing errors while calculang the age of a lm 196
Using PDI error handling funconality 200
Aborng a transformaon 201
Time for acon – aborng when there are too many errors 202
Aborng a transformaon using the Abort step 203
Fixing captured errors 203
Table of Contents
[ v ]
Time for acon – treang errors that may appear 203
Treang rows coming to the error stream 205
Avoiding unexpected errors by validang data 206
Time for acon – validang genres with a Regex Evaluaon step 206
Validang data 208
Time for acon – checking lms le with the Data Validator 209
Dening simple validaon rules using the Data Validator 211
Cleansing data 213
Summary 215
Chapter 8: Working with Databases 217
Introducing the Steel Wheels sample database 217
Connecng to the Steel Wheels database 219
Time for acon – creang a connecon with the Steel Wheels database 219
Connecng with Relaonal Database Management Systems 222
Exploring the Steel Wheels database 223
Time for acon – exploring the sample database 224
A brief word about SQL 225
Exploring any congured database with the PDI Database explorer 228
Querying a database 229
Time for acon – geng data about shipped orders 229
Geng data from the database with the Table input step 231
Using the SELECT statement for generang a new dataset 232
Making exible queries by using parameters 234
Time for acon – geng orders in a range of dates by using parameters 234
Making exible queries by using Kele variables 236
Time for acon – geng orders in a range of dates by using variables 237
Sending data to a database 239
Time for acon – loading a table with a list of manufacturers 239
Inserng new data into a database table with the Table output step 245
Inserng or updang data by using other PDI steps 246
Time for acon – inserng new products or updang existent ones 246
Time for acon – tesng the update of exisng products 249
Inserng or updang data with the Insert/Update step 251
Eliminang data from a database 256
Time for acon – deleng data about disconnued items 256
Deleng records of a database table with the Delete step 259
Summary 260
Chapter 9: Performing Advanced Operaons with Databases 261
Preparing the environment 261
Time for acon – populang the Jigsaw database 261
Exploring the Jigsaw database model 264
Table of Contents
[ vi ]
Looking up data in a database 266
Doing simple lookups 266
Time for acon – using a Database lookup step to create a list of products to buy 266
Looking up values in a database with the Database lookup step 268
Doing complex lookups 270
Time for acon – using a Database join step to create a list of
suggested products to buy 270
Joining data from the database to the stream data by using a Database join step 272
Introducing dimensional modeling 275
Loading dimensions with data 276
Time for acon – loading a region dimension with a
Combinaon lookup/update step 276
Time for acon – tesng the transformaon that loads the region dimension 279
Describing data with dimensions 281
Loading Type I SCD with a Combinaon lookup/update step 282
Keeping a history of changes 286
Time for acon – keeping a history of product changes with the
Dimension lookup/update step 286
Time for acon – tesng the transformaon that keeps a history
of product changes 288
Keeping an enre history of data with a Type II slowly changing dimension 289
Loading Type II SCDs with the Dimension lookup/update step 291
Summary 296
Chapter 10: Creang Basic Task Flows 297
Introducing PDI jobs 297
Time for acon – creang a simple hello world job 298
Execung processes with PDI jobs 305
Using Spoon to design and run jobs 306
Using the transformaon job entry 307
Receiving arguments and parameters in a job 309
Time for acon – customizing the hello world le with
arguments and parameters 309
Using named parameters in jobs 312
Running jobs from a terminal window 312
Time for acon – execung the hello world job from a terminal window 313
Using named parameters and command-line arguments in transformaons 314
Time for acon – calling the hello world transformaon with
xed arguments and parameters 315
Deciding between the use of a command-line argument and a named parameter 317
Running job entries under condions 318
Table of Contents
[ vii ]
Time for acon – sending a sales report and warning the
administrator if something is wrong 318
Changing the ow of execuon on the basis of condions 324
Creang and using a le results list 326
Summary 327
Chapter 11: Creang Advanced Transformaons and Jobs 329
Enhancing your processes with the use of variables 329
Time for acon – updang a le with news about examinaons by seng
a variable with the name of the le 330
Seng variables inside a transformaon 335
Enhancing the design of your processes 337
Time for acon – generang les with top scores 337
Reusing part of your transformaons 341
Time for acon – calculang the top scores with a subtransformaon 341
Creang and using subtransformaons 345
Creang a job as a process ow 348
Time for acon – spling the generaon of top scores by
copying and geng rows 348
Transferring data between transformaons by using the copy /get rows mechanism 352
Nesng jobs 354
Time for acon – generang the les with top scores by nesng jobs 354
Running a job inside another job with a job entry 355
Understanding the scope of variables 356
Iterang jobs and transformaons 357
Time for acon – generang custom les by execung a transformaon
for every input row 358
Execung for each row 361
Summary 366
Chapter 12: Developing and Implemenng a Simple Datamart 367
Exploring the sales datamart 367
Deciding the level of granularity 370
Loading the dimensions 370
Time for acon – loading dimensions for the sales datamart 371
Extending the sales datamart model 376
Loading a fact table with aggregated data 378
Time for acon – loading the sales fact table by looking up dimensions 378
Geng the informaon from the source with SQL queries 384
Translang the business keys into surrogate keys 388
Obtaining the surrogate key for a Type I SCD 388
Obtaining the surrogate key for a Type II SCD 389
Obtaining the surrogate key for the Junk dimension 391
Obtaining the surrogate key for the Time dimension 391
Table of Contents
[ viii ]
Geng facts and dimensions together 394
Time for acon – loading the fact table using a range of dates obtained
from the command line 394
Time for acon – loading the sales star 396
Geng rid of administrave tasks 399
Time for acon – automang the loading of the sales datamart 399
Summary 403
Chapter 13: Taking it Further 405
PDI best pracces 405
Geng the most out of PDI 408
Extending Kele with plugins 408
Overcoming real world risks with some remote execuon 410
Scaling out to overcome bigger risks 411
Integrang PDI and the Pentaho BI suite 412
PDI as a process acon 412
PDI as a datasource 413
More about the Pentaho suite 414
PDI Enterprise Edion and Kele Developer Support 415
Summary 416
Appendix A: Working with Repositories 417
Creang a repository 418
Time for acon – creang a PDI repository 418
Creang repositories to store your transformaons and jobs 420
Working with the repository storage system 421
Time for acon – logging into a repository 421
Logging into a repository by using credenals 422
Dening repository user accounts 422
Creang transformaons and jobs in repository folders 423
Creang database connecons, parons, servers, and clusters 424
Backing up and restoring a repository 424
Examining and modifying the contents of a repository with
the Repository explorer 424
Migrang from a le-based system to a repository-based system and
vice-versa 426
Summary 427
Appendix B: Pan and Kitchen: Launching Transformaons and
Jobs from the Command Line 429
Running transformaons and jobs stored in les 429
Running transformaons and jobs from a repository 430
Specifying command line opons 431
Table of Contents
[ ix ]
Checking the exit code 432
Providing opons when running Pan and Kitchen 432
Log details 433
Named parameters 433
Arguments 433
Variables 433
Appendix C: Quick Reference: Steps and Job Entries 435
Transformaon steps 436
Job entries 440
Appendix D: Spoon Shortcuts 443
General shortcuts 443
Designing transformaons and jobs 444
Grids 445
Repositories 445
Appendix E: Introducing PDI 4 Features 447
Agile BI 447
Visual improvements for designing transformaons and jobs 447
Experiencing the mouse-over assistance 447
Time for acon – creang a hop with the mouse-over assistance 448
Using the mouse-over assistance toolbar 448
Experiencing the sni-tesng feature 449
Experiencing the job drill-down feature 449
Experiencing even more visual changes 450
Enterprise features 450
Summary 450
Appendix F: Pop Quiz Answers 451
Chapter 1 451
PDI data sources 451
PDI prerequisites 451
PDI basics 451
Chapter 2 452
formang data 452
Chapter 3 452
concatenang strings 452
Chapter 4 452
data movement (copying and distribung) 452
spling a stream 452
Chapter 5 453
nding the seven errors 453
Table of Contents
[ x ]
Chapter 6 453
using Kele variables inside transformaons 453
Chapter 7 453
PDI error handling 453
Chapter 8 454
dening database connecons 454
database datatypes versus PDI datatypes 454
Insert/Update step versus Table Output/Update steps 454
ltering the rst 10 rows 454
Chapter 9 454
loading slowly changing dimensions 454
loading type III slowly changing dimensions 455
Chapter 10 455
dening PDI jobs 455
Chapter 11 455
using the Add sequence step 455
deciding the scope of variables 455
Chapter 12 456
modifying a star model and loading the star with PDI 456
Chapter 13 456
remote execuon and clustering 456
Index 457
Pentaho Data Integraon (aka Kele) is an engine along with a suite of tools responsible
for the processes of Extracng, Transforming, and Loading—beer known as the ETL
processes. PDI not only serves as an ETL tool, but it's also used for other purposes such as
migrang data between applicaons or databases, exporng data from databases to at
les, data cleansing, and much more. PDI has an intuive, graphical, drag-and-drop design
environment, and its ETL capabilies are powerful. However, geng started with PDI can be
dicult or confusing. This book provides the guidance needed to overcome that diculty,
covering the key features of PDI. Each chapter introduces new features, allowing you to
gradually get involved with the tool.
By the end of the book, you will have not only experimented with all kinds of examples, but
will also have built a basic but complete datamart with the help of PDI.
How to read this book
Although it is recommended that you read all the chapters, you don't need to. The book
allows you to tailor the PDI learning process according to your parcular needs.
The rst four chapters, along with Chapter 7 and Chapter 10, cover the core concepts. If
you don't know PDI and want to learn just the basics, reading those chapters would suce.
Besides, if you need to work with databases, you could include Chapter 8 in the roadmap.
If you already know the basics, you can improve your PDI knowledge by reading chapters 5,
6, and 11.
Finally, if you already know PDI and want to learn how to use it to load or maintain a
datawarehouse or datamart, you will nd all that you need in chapters 9 and 12.
While Chapter 13 is useful for anyone who is willing to take it further, all the appendices are
valuable resources for anyone who reads this book.
[ 2 ]
What this book covers
Chapter 1, Geng started with Pentaho Data Integraon serves as the most basic
introducon to PDI, presenng the tool. The chapter includes instrucons for installing PDI
and gives you the opportunity to play with the graphical designer (Spoon). The chapter also
includes instrucons for installing a MySQL server.
Chapter 2, Geng Started with Transformaons introduces one of the basic components
of PDI—transformaons. Then, it focuses on the explanaon of how to work with les. It
explains how to get data from simple input sources such as txt, csv, xml, and so on, do a
preview of the data, and send the data back to any of these common output formats. The
chapter also explains how to read command-line parameters and system informaon.
Chapter 3, Basic Data Manipulaon explains the simplest and most commonly used ways of
transforming data, including performing calculaons, adding constants, counng, ltering,
ordering, and looking for data.
Chapter 4—Controlling the Flow of Data explains dierent opons that PDI oers to combine
or split ows of data.
Chapter 5, Transforming Your Data with JavaScript Code and the JavaScript Step explains how
JavaScript coding can help in the treatment of data. It shows why you need to code inside
PDI, and explains in detail how to do it.
Chapter 6, Transforming the Row Set explains the ability of PDI to deal with some
sophiscated problems, such as normalizing data from pivoted tables, in a simple fashion.
Chapter 7, Validang Data and Handling Errors explains the dierent opons that PDI has to
validate data, and how to treat the errors that may appear.
Chapter 8, Working with Databases explains how to use PDI to work with databases. The
list of topics covered includes connecng to a database, previewing and geng data, and
inserng, updang, and deleng data. As database knowledge is not presumed, the chapter
also covers fundamental concepts of databases and the SQL language.
Chapter 9, Performing Advanced Operaons with Databases explains how to perform
advanced operaons with databases, including those specially designed to load
datawarehouses. A primer on datawarehouse concepts is also given in case you are not
familiar with the subject.
Chapter 10, Creang Basic Task Flow serves as an introducon to processes in PDI. Through
the creaon of simple jobs, you will learn what jobs are and what they are used for.
Chapter 11, Creang Advanced Transformaons and Jobs deals with advanced concepts that
will allow you to build complex PDI projects. The list of covered topics includes nesng jobs,
iterang on jobs and transformaons, and creang subtransformaons.
[ 3 ]
Chapter 12, Developing and implemenng a simple datamart presents a simple datamart
project, and guides you to build the datamart by using all the concepts learned throughout
the book.
Chapter 13, Taking it Further gives a list of best PDI pracces and recommendaons for
going beyond.
Appendix A, Working with repositories guides you step by step in the creaon of a PDI
database repository and then gives instrucons to work with it.
Appendix B, Pan and Kitchen: Launching Transformaons and Jobs from the Command Line is
a quick reference for running transformaons and jobs from the command line.
Appendix C, Quick Reference: Steps and Job Entries serves as a quick reference to steps and
job entries used throughout the book.
Appendix D, Spoon Shortcuts is an extensive list of Spoon shortcuts useful for saving me
when designing and running PDI jobs and transformaons.
Appendix E, Introducing PDI 4 features quickly introduces you to the architectural and
funconal features included in Kele 4—the version that was under development while
wring this book.
Appendix F, Pop Quiz Answers, contains answers to pop quiz quesons.
What you need for this book
PDI is a mulplaorm tool. This means no maer what your operang system is, you will
be able to work with the tool. The only prerequisite is to have JVM 1.5 or a higher version
installed. It is also useful to have Excel or Calc along with a nice text editor.
Having an Internet connecon while reading is extremely useful as well. Several links are
provided throughout the book that complement what is explained. Besides, there is the
PDI forum where you may search or post doubts if you are stuck with something.
Who this book is for
This book is for soware developers, database administrators, IT students, and everyone
involved or interested in developing ETL soluons or, more generally, doing any kind of data
manipulaon. If you have never used PDI before, this will be a perfect book to start with.
You will nd this book to be a good starng point if you are a database administrator, a data
warehouse designer, an architect, or any person who is responsible for data warehouse
projects and need to load data into them.
[ 4 ]
You don't need to have any prior data warehouse or database experience to read this book.
Fundamental database and data warehouse technical terms and concepts are explained in
an easy-to-understand language.
In this book, you will nd a number of styles of text that disnguish between dierent
kinds of informaon. Here are some examples of these styles, and an explanaon of
their meaning.
Code words in text are shown as follows: "You read the examination.txt le, and did
some calculaons to see how the students did."
New terms and important words are shown in bold. Words that you see on the screen, in
menus or dialog boxes for example, appear in our text like this: "Edit the Sort rows step by
double-clicking it, click the Get Fields buon, and adjust the grid."
Warnings or important notes appear in a box like this.
Tips and tricks appear like this.
Reader feedback
Feedback from our readers is always welcome. Let us know what you think about this book—
what you liked or may have disliked. Reader feedback is important for us to develop tles
that you really get the most out of.
To send us general feedback, simply drop an email to, and
menon the book tle in the subject of your message.
If there is a book that you need and would like to see us publish, please send us a note in the
SUGGEST A TITLE form on or email
If there is a topic that you have experse in and you are interested in either wring or
contribung to a book, see our author guide on
[ 5 ]
Customer support
Now that you are the proud owner of a Packt book, we have a number of things to help you
to get the most from your purchase.
Downloading the example code for the book
Visit to
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aspect of the book, and we will do our best to address it.
Getting Started with Pentaho
Data Integration
Pentaho Data Integraon is an engine along with a suite of tools responsible
for the processes of extracng, transforming, and loading—best known as the
ETL processes. This book is meant to teach you how to use PDI.
In this chapter you will:
Learn what Pentaho Data Integraon is
Install the soware and start working with the PDI graphical designer
Install MySQL, a database engine that you will use when you start working
with databases
Pentaho Data Integration and Pentaho BI Suite
Before introducing PDI, let's talk about Pentaho BI Suite. The Pentaho Business Intelligence
Suite is a collecon of soware applicaons intended to create and deliver soluons for
decision making. The main funconal areas covered by the suite are:
Analysis: The analysis engine serves muldimensional analysis. It's provided by the
Mondrian OLAP server and the JPivot library for navigaon and exploring.
Geng Started with Pentaho Data Integraon
[ 8 ]
Reporng: The reporng engine allows designing, creang, and distribung reports
in various known formats (HTML, PDF, and so on) from dierent kinds of sources.
The reports created in Pentaho are based mainly in the JFreeReport library, but it's
possible to integrate reports created with external reporng libraries such as Jasper
Reports or BIRT.
Data Mining: Data mining is running data through algorithms in order to understand
the business and do predicve analysis. Data mining is possible thanks to the
Weka Project.
Dashboards: Dashboards are used to monitor and analyze Key Performance
Indicators (KPIs). A set of tools incorporated to the BI Suite in the latest version
allows users to create interesng dashboards, including graphs, reports, analysis
views, and other Pentaho content, without much eort.
Data integraon: Data integraon is used to integrate scaered informaon
from dierent sources (applicaons, databases, les) and make the integrated
informaon available to the nal user. Pentaho Data Integraon—our main
concern—is the engine that provides this funconality.
All this funconality can be used standalone as well as integrated. In order to run analysis,
reports, and so on integrated as a suite, you have to use the Pentaho BI Plaorm. The
plaorm has a soluon engine, and oers crical services such as authencaon,
scheduling, security, and web services.
Chapter 1
[ 9 ]
This set of soware and services forms a complete BI Plaorm, which makes Pentaho Suite
the world's leading open source Business Intelligence Suite.
Exploring the Pentaho Demo
Despite being out of the scope of this book, it's worth to briey introduce the Pentaho
Demo. The Pentaho BI Plaorm Demo is a precongured installaon that lets you explore
several capabilies of the Pentaho plaorm. It includes sample reports, cubes, and
dashboards for Steel Wheels. Steel Wheels is a conal store that sells all kind of scale
replicas of vehicles.
The demo can be downloaded from
files/. Under the Business Intelligence Server folder, look for the latest stable
version. The le you have to download is named for
Windows and biserver-ce-3.5.2.stable.tar.gz for other systems.
In the same folder you will nd a le named biserver-getting_started-ce-
3.5.0.pdf. The le is a guide that introduces you the plaorm and gives you some
guidance on how to install and run it. The guide even includes a mini tutorial on building
a simple PDI input-output transformaon.
You can nd more about Pentaho BI Suite at
Pentaho Data Integration
Most of the Pentaho engines, including the engines menoned earlier, were created as
community projects and later adopted by Pentaho. The PDI engine is no excepon—Pentaho
Data Integraon is the new denominaon for the business intelligence tool born as Kele.
The name Kele didn't come from the recursive acronym Kele Extracon,
Transportaon, Transformaon, and Loading Environment it has now, but from
KDE Extracon, Transportaon, Transformaon and Loading Environment,
as the tool was planned to be wrien on top of KDE, as menoned in the
introducon of the book.
In April 2006 the Kele project was acquired by the Pentaho Corporaon and Ma Casters,
Kele's founder, also joined the Pentaho team as a Data Integraon Architect.
Geng Started with Pentaho Data Integraon
[ 10 ]
When Pentaho announced the acquision, James Dixon, the Chief Technology Ocer, said:
We reviewed many alternaves for open source data integraon, and Kele clearly
had the best architecture, richest funconality, and most mature user interface.
The open architecture and superior technology of the Pentaho BI Plaorm
and Kele allowed us to deliver integraon in only a few days, and make that
integraon available to the community.
By joining forces with Pentaho, Kele beneted from a huge developer community, as well
as from a company that would support the future of the project.
From that moment the tool has grown constantly. Every few months a new release is
available, bringing to the users, improvements in performance and exisng funconality,
new funconality, ease of use, and great changes in look and feel. The following is a meline
of the major events related to PDI since its acquision by Pentaho:
June 2006: PDI 2.3 is released. Numerous developers had joined the project and
there were bug xes provided by people in various regions of the world. Among
other changes, the version included enhancements for large scale environments
and mullingual capabilies.
February 2007: Almost seven months aer the last major revision, PDI 2.4 is
released including remote execuon and clustering support (more on this in
Chapter 13), enhanced database support, and a single designer for the two
main elements you design in Kele—jobs and transformaons.
May 2007: PDI 2.5 is released including many new features, the main feature being
the advanced error handling.
November 2007: PDI 3.0 emerges totally redesigned. Its major library changed to
gain massive performance. The look and feel also changed completely.
October 2008: PDI 3.1 comes with an easier-to-use tool, along with a lot of new
funconalies as well.
April 2009: PDI 3.2 is released with a really large number of changes for a
minor version—new funconality, visualizaon improvements, performance
improvements, and a huge pile of bug xes. The main change in this version was the
incorporaon of dynamic clustering (see Chapter 13 for details).
In 2010 PDI 4.0 will be released, delivering mostly improvements with regard to
enterprise features such as version control.
Most users sll refer to PDI as Kele, its further name. Therefore, the names PDI,
Pentaho Data Integraon, and Kele will be used interchangeably throughout
the book.
Chapter 1
[ 11 ]
Using PDI in real world scenarios
Paying aenon to its name, Pentaho Data Integraon, you could think of PDI as a tool to
integrate data.
In you look at its original name, K.E.T.T.L.E., then you must conclude that it is a tool used
for ETL processes which, as you may know, are most frequently seen in data warehouse
In fact, PDI not only serves as a data integrator or an ETL tool, but is such a powerful tool
that it is common to see it used for those and for many other purposes. Here you have
some examples.
Loading datawarehouses or datamarts
The loading of a datawarehouse or a datamart involves many steps, and there are many
variants depending on business area or business rules. However, in every case, the process
involves the following steps:
Extracng informaon from one or dierent databases, text les, and other sources.
The extracon process may include the task of validang and discarding data that
doesn't match expected paerns or rules.
Transforming the obtained data to meet the business and technical needs required
on the target. Transformaon implies tasks such as converng data types, doing
some calculaons, ltering irrelevant data, and summarizing.
Loading the transformed data into the target database. Depending on the
requirements, the loading may overwrite the exisng informaon, or may
add new informaon each me it is executed.
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Kele comes ready to do every stage of this loading process. The following sample
screenshot shows a simple ETL designed with Kele:
Integrating data
Imagine two similar companies that need to merge their databases in order to have a unied
view of the data, or a single company that has to combine informaon from a main ERP
applicaon and a CRM applicaon, though they're not connected. These are just two of
hundreds of examples where data integraon is needed. Integrang data is not just a maer
of gathering and mixing data; some conversions, validaon, and transport of data has to be
done. Kele is meant to do all those tasks.
Data cleansing
Why do we need that data be correct and accurate? There are many reasons—for the
eciency of business, to generate trusted conclusions in data mining or stascal studies,
to succeed when integrang data, and so on. Data cleansing is about ensuring that the
data is correct and precise. This can be ensured by verifying if the data meets certain rules,
discarding or correcng those that don't follow the expected paern, seng default values
for missing data, eliminang informaon that is duplicated, normalizing data to conform
minimum and maximum values, and so on—tasks that Kele makes possible, thanks to its
vast set of transformaon and validaon capabilies.
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Migrating information
Think of a company of any size that uses a commercial ERP applicaon. One day the owners
realize that the licences are consuming an important share of its budget and so they decide
to migrate to an open source ERP. The company will no longer have to pay licences, but if
they want to do the change, they will have to migrate the informaon. Obviously it is not an
opon to start from scratch, or type the informaon by hand. Kele makes the migraon
possible, thanks to its ability to interact with most kinds of sources and desnaons such as
plain les, and commercial and free databases and spreadsheets.
Exporting data
Somemes you are forced by government regulaons to export certain data to be processed
by legacy systems. You can't just print and deliver some reports containing the required data.
The data has to have a rigid format, with columns that have to obey some rules (size, format,
content), dierent records for heading and tail, just to name some common demands. Kele
has the power to take crude data from the source and generate these kinds of ad hoc reports.
Integrating PDI using Pentaho BI
The previous examples show typical uses of PDI as a standalone applicaon. However, Kele
may be used as part of a process inside the Pentaho BI Plaorm. There are many things
embedded in the Pentaho applicaon that Kele can do—preprocessing data for an on-line
report, sending mails in a schedule fashion, or generang spreadsheet reports.
You'll nd more on this in Chapter 13. However, the use of PDI integrated
with the BI Suite is beyond the scope of this book.
Pop quiz – PDI data sources
Which of the following aren't valid sources in Kele:
1. Spreadsheets
2. Free database engines
3. Commercial database engines
4. Flat les
5. None of the above
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[ 14 ]
Installing PDI
In order to work with PDI you need to install the soware. It's a simple task; let's do it.
Time for action – installing PDI
These are the instrucons to install Kele, whatever your operang system.
The only prerequisite to install PDI is to have JRE 5.0 or higher installed. If you don't have it,
please download it from and install it before proceeding.
Once you have checked the prerequisite, follow these steps:
1. From follow the link to
Pentaho Data Integraon (Kele). Alternavely, go directly to the download page Integration.
2. Choose the newest stable release. At this me, it is 3.2.0.
3. Download the le that matches your plaorm. The preceding screenshot should
help you.
4. Unzip the downloaded le in a folder of your choice
C:/Kettle or /home/your_dir/kettle.
Chapter 1
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5. If your system is Windows, you're done. Under UNIX-like environments, it's
recommended that you make the scripts executable. Assuming that you
chose Kele as the installaon folder, execute the following command:
cd Kettle
chmod +x *.sh
What just happened?
You have installed the tool in just a few minutes. Now you have all you need to start working.
Pop quiz – PDI prerequisites
Which of the following are mandatory to run PDI? You may choose more than one opon.
1. Kele
2. Pentaho BI plaorm
3. JRE
4. A database engine
Launching the PDI graphical designer: Spoon
Now that you've installed PDI, you must be eager to do some stu with data. That will be
possible only inside a graphical environment. PDI has a desktop designer tool named Spoon.
Let's see how it feels to work with it.
Time for action – starting and customizing Spoon
In this tutorial you're going to launch the PDI graphical designer and get familiarized with itsn this tutorial you're going to launch the PDI graphical designer and get familiarized with its
main features.
1. Start Spoon.
If your system is Windows, type the following command:
In other plaorms such as Unix, Linux, and so on, type:
If you didn't make executable, you may type:
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[ 16 ]
2. As soon as Spoon starts, a dialog window appears asking for the repository
connecon data. Click the No Repository buon. The main window appears. You
will see a small window with the p of the day. Aer reading it, close that window.
3. A welcome! window appears with some useful links for you to see.
4. Close the welcome window. You can open that window later from the main menu.
5. Click Opons... from the Edit menu. A window appears where you can change
various general and visual characteriscs. Uncheck the circled checkboxes:
6. Select the tab window Look Feel.
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7. Change the Grid size and Preferred Language sengs as follows:
8. Click the OK buon.
9. Restart Spoon in order to apply the changes. You should neither see the repository
dialog, nor the welcome window. You should see the following screen instead:
Geng Started with Pentaho Data Integraon
[ 18 ]
What just happened?
You ran for the rst me the graphical designer of PDI Spoon, and applied some
custom conguraon.
From the Look Feel conguraon window, you changed the size of the doed grid that
appears in the canvas area while you are working. You also changed the preferred language.
In the Opon tab window, you chose not to show either the repository dialog or the
welcome window at startup. These changes were applied as you restarted the tool, not
The second me you launched the tool, the repository dialog didn't show up. When the
main window appeared, all the visible texts were shown in French, which was the selected
language, and instead of the welcome window, there was a blank screen.
This tool that you're exploring in this secon is the PDI's desktop design tool. With Spoon you
design, preview, and test all your work, that is, transformaons and jobs. When you see PDI
screenshots, what you are really seeing are Spoon screenshots. The other PDI components
that you will meet in the following chapters are executed from terminal windows.
Setting preferences in the Options window
In the tutorial you changed some preferences in the Opons window. There are several look
and feel characteriscs you can change beyond those you changed. Feel free to experiment
with this seng.
Remember to restart Spoon in order to see the changes applied.
If you choose any language as preferred language other than English, you
should select a dierent language as alternave. If you do so, every name or
descripon not translated to your preferred language will be shown in the
alternave language.
Just for the curious people: Italian and French are the overall winners of the list of languages
to which the tool has been translated from English. Below them follow Korean, Argennean
Spanish, Japanese, and Chinese.
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One of the sengs you changed was the appearance of the welcome window at start up.
The welcome window has many useful links, all related with the tool: wiki pages, news,
forum access, and more. It's worth exploring them.
You don't have to change the sengs again to see the welcome window.
You can open it from the menu Help | Show the Welcome Screen.
Storing transformations and jobs in a repository
The rst me you launched Spoon, you chose No Repository. Aer that, you congured
Spoon to stop asking you for the Repository opon. You must be curious about what the
repository is and why not to use it. Let's explain it.
As said, the results of working with PDI are Transformaons and Jobs. In order to save the
Transformaons and Jobs, PDI oers two methods:
Repository: When you use the repository method you save jobs and
transformaons in a repository. A repository is a relaonal database specially
designed for this purpose.
Files: The les method consists of saving jobs and transformaons as regular XML
les in the lesystem, with extension kjb and ktr respecvely.
The following diagram summarizes this:
.ktr .kjb
Design, Preview, Run
Kettle Engine KETTLE
mations Jobs
Transformations Jobs
Design, Preview, Run
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You cannot mix the two methods (les and repository) in the same project. Therefore, you
must choose the method when you start the tool.
Why did we choose not to work with repository, or in other words, to work with les? This is
mainly for the following two reasons:
Working with les is more natural and praccal for most users.
Working with repository requires minimum database knowledge and that you also
have access to a database engine from your computer. Having both precondions
would allow you to learn working with both methods. However, it's probable that
you haven't.
Throughout this book, we will use the le method. For details of working with repositories,
please refer to Appendix A.
Creating your rst transformation
Unl now, you've seen the very basic elements of Spoon. For sure, you must be waing to do
some interesng task beyond looking around. It's me to create your rst transformaon.
Time for action – creating a hello world transformation
How about starng by saying Hello to the World? Not original but enough for a very rst
praccal exercise. Here is how you do it:
1. Create a folder named pdi_labs under the folder of your choice.
2. Open Spoon.
3. From the main menu select File | New Transformaon.
4. At the le-hand side of the screen, you'll see a tree of Steps. Expand the Input
branch by double-clicking it.
5. Le-click the Generate Rows icon.
Chapter 1
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6. Without releasing the buon, drag-and-drop the selected icon to the main canvas.
The screen will look like this:
7. Double-click the Generate Rows step that you just put in the canvas and ll the text
boxes and grid as follows:
8. From the Steps tree, double-click the Flow step.
9. Click the Dummy icon and drag-and-drop it to the main canvas.
Geng Started with Pentaho Data Integraon
[ 22 ]
10. Click the Generate Rows step and holding the Shi key down, drag the cursor
towards the Dummy step. Release the buon. The screen should look like this:
11. Right-click somewhere on the canvas to bring up a contextual menu.
12. Select New note. A note editor appears.
13. Type some descripon such as Hello World! and click OK.
14. From the main menu, select Transformaon | Conguraon. A window appears
to specify transformaon properes. Fill the Transformaon name with a simple
name as hello_world. Fill the Descripon eld with a short descripon such as
My rst transformaon. Finally provide a more clear explanaon in the Extended
descripon text box and click OK.
15. From the main menu, select File | Save.
16. Save the transformaon in the folder pdi_labs with the name hello_world.
17. Select the Dummy step by le-clicking it.
18. Click on the Preview buon in the menu above the main canvas.
Chapter 1
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19. A debug window appears. Click the Quick Launch buon.
20. The following window appears to preview the data generated by the transformaon:
21. Close the preview window and click the Run buon.
22. A window appears. Click Launch.
Geng Started with Pentaho Data Integraon
[ 24 ]
23. The execuon results are shown in the boom of the screen. The Logging tab
should look as follows:
What just happened?
You've just created your rst transformaon.
First, you created a new transformaon. From the tree on the le, you dragged two steps
and drop them into the canvas. Finally, you linked them with a hop.
With the Generate Rows step, you created 10 rows of data with the message Hello World!.
The Dummy step simply served as a desnaon of those rows.
Aer creang the transformaon, you did a preview. The preview allowed you to see the
content of the created data, this is, the 10 rows with the message Hello World!
Chapter 1
[ 25 ]
Finally, you ran the transformaon. You could see the results of the execuon at the boom
of the windows. There is a tab named Step Metrics with informaon about what happens
with each steps in the transformaon. There is also a Logging tab showing a complete detail
of what happened.
Directing the Kettle engine with transformations
As shown in the following diagram, transformaon is an enty made of steps linked by hops.
These steps and hops build paths through which data ows. The data enters or is created in a
step, the step applies some kind of transformaon to it, and nally the data leaves that step.
Therefore, it's said that a transformaon is data-ow oriented.
Step1 Step2 StepN
A transformaon itself is not a program nor an executable le. It is just plain XML. The
transformaon contains metadata that tells the Kele engine what to do.
A step is the minimal unit inside a transformaon. A big set of steps is available. These steps
are grouped in categories such as the input and ow categories that you saw in the example.
Each step is conceived to accomplish a specic funcon, going from reading a parameter to
normalizing a dataset. Each step has a conguraon window. These windows vary according
to the funconality of the steps and the category to which they belong. What all steps have
in common are the name and descripon:
Step property Descripon
Name A representative name inside the transformation.
Description A brief explanation that allows you to clarify the purpose of the step.
It's not mandatory but it is useful.
A hop is a graphical representaon of data owing between two steps—an origin and a
desnaon. The data that ows through that hop constutes the output data of the origin
step and the input data of the desnaon step.
Geng Started with Pentaho Data Integraon
[ 26 ]
Exploring the Spoon interface
As you just saw, the Spoon is the tool using which you create, preview, and run
transformaons. The following screenshot shows you the basic work areas:
The words canvas and work area will be used interchangeably throughout
the book.
Viewing the transformation structure
If you click the View icon in the upper le corner of the screen, the tree will change to show
the structure of the transformaon currently being edited.
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[ 27 ]
Running and previewing the transformation
The Preview funconality allows you to see a sample of the data produced for selected steps.
In the previous example, you previewed the output of the Dummy Step. The Run opon
eecvely runs the whole transformaon.
Whether you preview or run a transformaon, you'll get an execuon results window
showing what happened. Let's explain it through an example.
Time for action – running and previewing the hello_world
Let's do some tesng and explore the results:
1. Open the hello_world transformaon.
2. Edit the Generate Rows step, and change the limit from 10 to 1000 so that it
generates 1,000 rows.
3. Select the Logging tab window at the boom of the screen.
4. Click on Run.
5. In the Log level drop-down list, select RowLevel detail.
6. Click on Launch.
7. You can see how the logging window shows every task in a very detailed way.
8. Edit the Generate Rows step, and change the limit to 10,000 so that it generates
10,000 rows.
9. Select the Step Metrics.
Geng Started with Pentaho Data Integraon
[ 28 ]
10. Run the transformaon.
11. You can see how the numbers change as the rows travel through the steps.
What just happened?
You did some tests with the hello_world transformaon and saw the results in the
Execuon Results window.
Previewing the results in the Execution Results window
The Execuon Results window shows you what is happening while you preview or run
a transformaon.
The Logging tab shows the execuon of your transformaon, step by step. By default, the
level of the logging detail is Basic but you can change it to see dierent levels of detail—from
a minimal logging (level Minimal) to a very detailed one (level RowLevel).
The Step Metrics tab shows, for each step of the transformaon, the executed operaons
and several status and informaon columns. You may be interested in the following columns:
Column Descripon
Read Contains the number of rows coming from previous steps
Written Contains the number of rows leaving from this step toward the next
Input Number of rows read from a le or table
Output Number of rows written to a le or table
Errors Errors in the execution. If there are errors, the whole row becomes red
Active Tells the current status of the execution
In the example, you can see that the Generate Rows step writes rows, which then are read
by the Dummy step. The Dummy step also writes the same rows, but in this case those
go nowhere.
Chapter 1
[ 29 ]
Pop quiz – PDI basics
For each of the following, decide if the sentence is true or false:
1. There are several graphical tools in PDI, but Spoon is the most used.
2. You can choose to save Transformaons either in les or in a database.
3. To run a Transformaon, an executable le has to be generated from Spoon.
4. The grid size opon in the Look and Feel windows allows you to resize the work area.
5. To create a transformaon, you have to provide external data.
Installing MySQL
Before skipping to the next chapter, let's devote some minutes to the installaon of MySQL.
In Chapter 8 you will begin working with databases from PDI. In order to do that, you will
need access to some database engine. As MySQL is the world's most popular open source
database, it was the database engine chosen for the database-related tutorials in the book.
In this secon you will learn to install the MySQL database engine both in Windows and
Ubuntu, the most popular distribuon of Linux these days. As the procedures for installing
the soware are dierent, a separate explanaon is given for each system.
Time for action – installing MySQL on Windows
In order to install MySQL on your Windows system, please follow these instrucons:
1. Open an internet browser and type
2. Select the Microso Windows plaorm and download the mysql-essenal package
that matches your system: 32-bit or 64-bit.
3. Double-click the downloaded le. A wizard will guide you through the process.
4. When asked about the setup type, select Typical.
5. Several screens follow. When the wizard is complete you'll have the opon to
congure the server. Check Congure the MySQL Server now and click Finish.
Geng Started with Pentaho Data Integraon
[ 30 ]
6. A new wizard will be launched that lets you congure the server.
7. When asked about the conguraon type, select Standard Conguraon.
8. When prompted, set the Windows opons as shown in the next screenshot:
9. When prompted for the security opons, provide a password for the root user.
You'll have to retype the password.
Provide a password that you can remember. You'll need it
later to connect to the MySQL server.
Chapter 1
[ 31 ]
10. In the next window click on Execute to proceed with the conguraon. When the
conguraon is done, you'll see this:
11. Click on Finish. Aer installing MySQL it is recommended that you install the GUI
tools for administering and querying the database.
12. Open an Internet browser and type
13. Look for the Windows downloads and download the Windows (x86) package.
14. Double-click the downloaded le. A wizard will guide you through the process.
15. When asked about the setup type, select Complete.
16. Several screens follow. Just follow the wizard instrucons.
17. When the wizard ends, you'll have the GUI tools added to the MySQL menu.
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[ 32 ]
What just happened?
You downloaded and installed MySQL on your Windows system. You also installed MySQL
GUI tools, a soware package that includes an administrator and a query browser ulity and
that will make your life easier when working with the database.
Time for action – installing MySQL on Ubuntu
This tutorial shows you the procedure to install MySQL on Ubuntu.
In order to follow the tutorial you need to be connected to
the Internet.
Please follow these instrucons:
1. Check that you have access to the Internet.
2. Open the Synapc package manager from System | Administraon | Synapc
Package Manager.
3. Under Quick search type mysql-server and click on the Search buon.
4. Among the results, locate mysql-server-5.1, click in the ny square to the le,
and select Mark for Installaon.
5. You'll be prompted for conrmaon. Click on Mark.
Chapter 1
[ 33 ]
6. Now search for a package named mysql-admin.
7. When found, mark it for installaon in the same way.
8. Click on Apply on the main toolbar.
9. A window shows up asking for conrmaon. Click on Mark again. What follows is
the download process followed by the installaon process.
10. At a parcular moment a window appears asking you for a password for the root
user—the administrator of the database. Enter a password of your choice. You'll
have to enter it twice.
Think of a password that you can remember. You'll need it
later to connect to the MySQL server.
11. When the process ends, you will see the changes applied.
Geng Started with Pentaho Data Integraon
[ 34 ]
12. Under Applicaons a new menu will also be added to access the GUI tools.
What just happened?
You installed MySQL server and GUI Tools in your Ubuntu system.
The previous direcons are for standard installaons. For custom installaons,
instrucons related to other operang systems, or for troubleshoong, please
check the MySQL documentaon at—
In this rst chapter, you were introduced to Pentaho Data Integraon. Specically, you learned
what Pentaho Data Integraon is and you installed the tool. You were also introduced to
Spoon, the graphical designer of PDI, and you created your rst transformaon.
As an addional exercise, you installed a MySQL server and the MySQL GUI tools. You will
need this soware when you start working with databases in Chapter 8.
Now that you've learned the basics, you're ready to begin creang your own transformaons
to explore real data. That is the topic of the next chapter.
Getting Started with Transformations
In the previous chapter you used the graphical designer Spoon to create
your rst transformaon: Hello world. Now you will start creang your own
transformaons to explore data from the real world. Data is everywhere; in
parcular you will nd data in les. Product lists, logs, survey results, and
stascal informaon are just a sample of the dierent kinds of informaon
usually stored in les. In this chapter you will create transformaons to get
data from les, and also to send data back to les. This in turn will allow you to
learn the basic PDI terminology related to data.
Reading data from les
Despite being the most primive format used to store data, les are broadly used and they
exist in several avors as xed width, comma-separated values, spreadsheet, or even free
format les. PDI has the ability to read data from all types of les; in this rst tutorial let's
see how to use PDI to get data from text les.
Geng Started with Transformaons
[ 36 ]
Time for action – reading results of football matches from les
Suppose you have collected several football stascs in plain les. Your les look like this:
Group|Date|Home Team |Results|Away Team|Notes
Group 1|02/June|Italy|2-1|France|
Group 1|02/June|Argentina|2-1|Hungary
Group 1|06/June|Italy|3-1|Hungary
Group 1|06/June|Argentina|2-1|France
Group 1|10/June|France|3-1|Hungary
Group 1|10/June|Italy|1-0|Argentina
World Cup 78
Group 1
You don't have one, but many les, all with the same structure. You now want to unify all the
informaon in one single le. Let's begin by reading the les.
1. Create the folder named pdi_files. Inside it, create the input and
output subfolders.
2. By using any text editor, type the le shown and save it under the name
group1.txt in the folder named input, which you just created. You can also
download the le from Packt's ocial website.
3. Start Spoon.
4. From the main menu select File | New Transformaon.
5. Expand the Input branch of the steps tree.
6. Drag the Text le input icon to the canvas.
7. Double-click the text input le icon and give a name to the step.
8. Click the Browse... buon and search the le group1.txt.
9. Select the le. The textbox File or directory will be temporarily populated with the full
path of the le—for example, C:\pdi_files\input\group1.txt.
Chapter 2
[ 37 ]
10. Click the Add buon. The full text will be moved from the File or directory textbox to the
grid. The conguraon window should look as follows:
11. Select the Content tab and ll it like this:
Geng Started with Transformaons
[ 38 ]
12. Select the Fields tab. Click the Get Fields buon. The screen should look like this:
13. In the small window that proposes you a number of sample lines, click OK.
14. Close the scan results window.
15. Change the second row. Under the Type column select Date, and under the Format
column, type dd/MMM.
16. The result value is text, not a number, so change the fourth row too. Under the Type
column select String.
17. Click the Preview rows buon, and then the OK buon.
18. The previewed data should look like the following:
Chapter 2
[ 39 ]
19. Expand the Transform branch of the steps tree.
20. Drag the Select values icon to the canvas.
21. Create a hop from the Text le input step to the Select values step.
Remember that you do it by selecng the rst step, then dragging
toward the second while holding down the Shi key.
22. Double-click the Select values step icon and give a name to the step.
23. Select the Remove tab.
24. Click the Get elds to remove buon.
25. Delete every row except the rst and the last one by le-clicking them and
pressing Delete.
26. The tab window looks like this:
27. Click OK.
28. From the Flow branch of the steps tree, drag the Dummy icon to the canvas.
29. Create a hop from the Select values step to the Dummy step. Your transformaon
should look like the following:
Geng Started with Transformaons
[ 40 ]
30. Congure the transformaon by pressing Ctrl+T and giving a name and a descripon to
the transformaon.
31. Save the transformaon by pressing Ctrl+S.
32. Select the Dummy step.
33. Click the Preview buon located on the transformaon toolbar:
34. Click the Quick Launch buon.
35. The following window appears, showing the nal data:
What just happened?
You read your plain le with results of football matches into a transformaon.
By using a Text le input step, you told Kele the full path to your le, along with the
characteriscs of the le so that Kele was able to read the data correctly—you specied
that the le had a header, had three rows at the end that should be ignored, and specied
the name and type of the columns.
Aer reading the le, you used a Select values step to remove columns you didn't need— the
rst and the last column.
Chapter 2
[ 41 ]
With those two simple steps, you were able to preview the data in your le from inside
the transformaon.
Another thing you may have noced is the use of shortcuts instead of the menu opons—for
example, to save the transformaon.
Many of the menu opons can be accessed more quickly by using shortcuts. The
available shortcuts for the menu opons are menoned as part of the name of
the operaon—for example, Run F9.
For a full shortcut reference please check Appendix D.
Input les
Files are one of the most used input sources. PDI can take data from several types of les,
with very few limitaons.
When you have a le to work with, the rst thing you have to do is to specify where the le
is, how it looks, and what kinds of values it contains. That is exactly what you did in the rst
tutorial of this chapter.
With the informaon you provide, Kele can create the dataset to work within the
current transformaon.
Input steps
There are several steps that allow you to take a le as the input data. All those steps such as
Text le input, Fixed le input, Excel Input, and so on are under the Input step category.
Despite the obvious dierences that exist between these types of les, the ways to congure
the steps have much in common. The following are the main properes you have to specify
for an input step:
Name of the step: It is mandatory and must be dierent for every step in
the transformaon.
Name and locaon of the le: These must be specied of course. At the moment
you create the transformaon, it's not mandatory that the le exists. However, if it
does, you will nd it easier to congure this step.
Content type: This data includes delimiter character, type of encoding, whether a
header is present, and so on. The list depends on the kind of le chosen. In every
case, Kele propose default values, so you don't have to enter too much data.
Geng Started with Transformaons
[ 42 ]
Fields: Kele has the facility to get the denions automacally by clicking the Get
Fields buon. However, Kele doesn't always guess the data types, size, or format
as expected. So, aer geng the elds you may change what you consider more
appropriate, as you did in the tutorial.
Filtering: Some steps allow you to lter the data—skip blank rows, read only the rst
n rows, and so on.
Aer conguring an input step, you can preview the data just as you did, by Clicking
the Preview Rows buon. This is useful to discover if there is something wrong in the
conguraon. In that case, you can make the adjustments and preview again, unl your
data looks ne.
Reading several les at once
Unl now you used an input step to read one le. But you have several les, all with the very
same structure. That will not be a problem because with Kele it is possible to read more
than a le at a me.
Time for action – reading all your les at a time using a single
Text le input step
To read all your les follow the next steps:
1. Open the transformaon, double-click the input step, and add the other les in the
same way you added the rst.
2. Aer Clicking the Preview rows buon, you will see this:
Chapter 2
[ 43 ]
What just happened?
You read several les at once. By pung in the grid the names of all the input les, you could
get the content of every specied le one aer the other.
Time for action – reading all your les at a time using a single
Text le input step and regular expressions
You could do the same thing you did above by using a dierent notaon.
Follow these instrucons:
1. Open the transformaon and edit the conguraon windows of the input step.
2. Delete the lines with the names of the les.
3. In the rst row of the grid, type C:\pdi_files\input\ under the File/Directory
column, and group[1-4]\.txt under the Wildcard (Reg.Exp.) column.
4. Click the Show lename(s)... buon. You'll see the list of les that match
the expression.
5. Close the ny window and click Preview rows to conrm that the rows shown
belong to the four les that match the expression you typed.
Geng Started with Transformaons
[ 44 ]
What just happened?
In this parcular case, all lenames follow a paern—group1.txt, group2.txt, and so
on. In order to specify the names of the les, you used a regular expression. In the column
File/Directory you put the stac part of the names, while in the Wildcard (Reg.Exp.) column
you put the regular expression with the paern that a le must follow to be considered:
the text group followed by a number between 1 and 4, and then .txt. Then, all les that
matched the expression were considered as input les.
Regular expressions
There are many places inside Kele where you may or have to provide a regular expression.
A regular expression is much more than specifying the known wildcards ? and *.
Here you have some examples of regular expressions you may use to specify lenames:
The following regular
expression ...
Matches ... Examples
.*\.txt Any txt le thisisaValidExample.
Any txt le beginning with test
followed by a date using the format
(?i)test.+\.txt Any txt le beginning with test,
upper or lower case
Please note that the * wildcard doesn't work the same as it does on
the command line. If you want to match any character, the * has to be
preceded by a dot.
Here are some useful links in case you want to know more about regular expressions:
Regular Expression Quick Start:
The Java Regular Expression Tutorial:
Java Regular Expression Paern Syntax:
Chapter 2
[ 45 ]
Troubleshooting reading les
Despite the simplicity of reading les with PDI, obstacles and errors appear. Many mes
the soluon is simple but dicult to nd if you are new to PDI. Here you have a list of
common problems and possible soluons for you to take into account while reading and
previewing a le:
Problem Diagnosc Possible soluons
You get the message
Sorry, no rows found to
be previewed.
This happens when the input le
doesn't exist or is empty.
It also may happen if you
specied the input les with
regular expressions and there
is no le that matches the
Check the name of the input les.
Verify the syntax used, check that
you didn't put spaces or any strange
character as part of the name.
If you used regular expressions, check
the syntax.
Also verify that you put the lename
in the grid. If you just put it in the File
or directory textbox, Kele will not
read it.
When you preview the
data you see a grid with
blank lines
The le contains empty lines, or
you forgot to get the elds.
Check the content of the le.
Also check that you got the elds in the
Fields tab.
You see the whole line
under the rst dened
You didn't set the proper
separator and Kele couldn't split
the dierent elds.
Check and x the separator in the
Content tab.
You see strange
You le the default content but
your le has a dierent format or
Check and x the Format and Encoding
in the Content tab.
If you are not sure of the format, you
can specify mixed.
You don't see all the
lines you have in the le
You are previewing just a sample
(100 lines by default).
Or you put a limit to the number
of rows to get.
Another problem may be that you
set the wrong number of header
or footer lines.
When you preview, you see just a
sample. This is not a problem.
If you raise the previewed number of
rows and sll have few lines, check the
Header, Footer and Limit opons in
the Content tab.
Geng Started with Transformaons
[ 46 ]
Problem Diagnosc Possible soluons
Instead of rows of
data, you get a window
headed ERROR with an
extract of the log
Dierent errors may happen, but
the most common has to do with
problems in the denion of the
You could try to understand the log
and x the denion accordingly. For
example if you see:
Couldn't parse eld [Integer] with
value [Italy].
The error is that PDI found the text
Italy in a eld that you dened as
If you made a mistake, you could x
it. On the other hand, if the le has
errors, you could read all elds as
String and you will not get the error
again. In chapter 7 you will learn how
to overcome these situaons.
Grids are tables used in many Spoon places to enter or display informaon. You already saw
grids in several conguraon windows—Text le input, Text le output, and Select values.
Many grids contain eld informaon. Examples of these grids are the Field tab window in the
Text Input and Output steps, or the main conguraon window of the Select Values step. In
these cases, the grids are usually accompanied by a Get Fields buon. The Get Fields buon
is a facility to avoid typing. When you press that buon, Kele lls the grid with all the
available elds.
For example, when reading a le, the Get Fields buon lls the grid with the columns of the
incoming le. When using a Select Values step or a File output step, the Get Fields buon
lls the grid with all the elds entering from a previous step.
Every me you see a Get Fields buon, consider it as a shortcut to avoid typing.
Kele will bring the elds available to the grid; you will only have to check the
informaon brought and make minimal changes.
There are many places in Spoon where the grid serves also to edit other kinds of informaon.
One example of that is the grid where you specify the list of les in a Text File Input step. No
maer what kind of grid you are eding, there is always a contextual menu, which you may
access by right-clicking on a row. That menu oers eding opons to copy, paste, or move
rows of the grid.
Chapter 2
[ 47 ]
When the number of rows in the grid is big, use shortcuts! Most of the eding
opons of a grid have shortcuts that make the eding work easier and quicker.
You'll nd a full list of shortcuts for eding grids in Appendix E.
Have a go hero – explore your own les
Try to read your own text les from Kele. You must have several les with dierent kinds of
data, dierent separators, and with or without header or footer. You can also search for les
over the Internet; there are plenty of les there to download and play with. Aer conguring
the input step, do a preview. If the data is not shown properly, x the conguraon and
preview again unl you are sure that the data is read as expected. If you have trouble
reading the les, please refer to the Troubleshoong reading les secon seen earlier for
diagnosis and possible ways to solve the problems.
Sending data to les
Now you know how to bring data into Kele. You didn't bring the data just to preview it; you
probably want to do some transformaon on the data, to nally send it to a nal desnaon
such as another plain le. Let's learn how to do this last task.
Time for action – sending the results of matches to a plain le
In the previous tutorial, you read all your "results of matches" les. Now you want to send
the data coming from all les to a single output le.
1. Create a new transformaon.
2. Drag a Text le input step to the canvas and congure it just as you did in the
previous tutorial.
3. Drag a Select values step to the canvas and create a hop from the Text le input
step to the Select values step.
4. Double-click the Select values step.
5. Click the Get elds to select buon.
Geng Started with Transformaons
[ 48 ]
6. Modify the elds as follows:
7. Expand the Output branch of the steps tree.
8. Drag the Text le output icon to the canvas.
9. Create a hop from the Select values step to the Text le output step.
10. Double-click the Text le output step and give it a name.
11. In the le name type: C:/pdi_files/output/wcup_first_round.
Note that the path contains forward slashes. If your system is Windows,
you may use back or forward slashes. PDI will recognize both notaons.
12. In the Content tab, leave the default values.
13. Select the Fields tab and congure it as follows:
Chapter 2
[ 49 ]
14. Click OK.
15. Give a name and descripon to the transformaon.
16. Save the transformaon.
17. Click Run and then Launch.
18. Once the transformaon is nished, check the le generated. It should have been
created as C:/pdi_files/output/wcup_first_round.txt and should look
like this:
Match Date;Home Team;Away Team;Result
01/06;Germany FR;Poland;0-0
06/06;Germany FR;Mexico;6-0
What just happened?
You gathered informaon from several les and sent all the data to a single le. Before
sending the data out, you used a Select Value step to select the data you wanted for the le
and to rename the elds so that the header of the desnaon le looks clearer.
Output les
We saw that PDI could take data from several types of les. The same applies to output data.
The data you have in a transformaon can be sent to dierent types of les. All you have to
do is redirect the ow of data towards an Output step.
Geng Started with Transformaons
[ 50 ]
Output steps
There are several steps that allow you to send the data to a le. All those steps are under the
Output step category: Text le output and Excel Output are examples of them.
For an Output step, just like you do for an Input step, you also have to dene:
Name of the step: It is mandatory and must be dierent for every step in
the transformaon.
Name and locaon of the le: These must be specied. If you specify an exisng
le, the le will be replaced by a new one (unless you check the Append checkbox
present in some of the output steps).
Content type: This data includes delimiter character, type of encoding, whether to
put a header, and so on. The list depends on the kind of le chosen. If you check
Header, the header will be built with the names of the elds.
If you don't like the names of the elds as header names in your le,
you may use a Select values step just to rename those elds.
Fields: Here you specify the list of elds that has to be sent to the le, and provide
some format instrucons. Just like in the input steps, you may use the Get Fields
buon to ll the grid. In this case, the grid is going to be lled based on the data
that arrives from the previous step. You are not forced to send every piece of data
coming to the output step, nor to send the elds in the same order.
Some data denitions
From the Kele's point of view, data can be anything ready to be processed by soware (for
example les or data in databases). Whichever the subject or origin of the data, whichever
its format, Kele transformaons can get the data for further processing and delivering.
Transformaons deals with datasets, that is, data presented in a tabular form, where:
Each column represents a eld. A eld has a name and a data type. The data type
can be any of the common data types—number (oat), string, date, Boolean, integer,
or big number.
Each row corresponds to a given member of the dataset. All rows in a dataset have
the same structure, that is, all rows have the same elds, in the same order. A eld
in a row may be null, but it has to be present.
Chapter 2
[ 51 ]
The dataset is called rowset. The following is an example of rowset. It is the rowset
generated in the World Cup tutorial:
Once the data is read, it travels from step to step, through the hops that link those steps.
Nothing happens in the hops except data owing. The real manipulaon of data, as well as
the modicaon of a stream by adding or removing columns, occurs in the steps.
Right-click on the Select values step of the transformaon you created. In the contextual
menu select Show output elds. You'll see this:
This window shows the metadata of the data that leaves this step, this is, name, type, and
other properes of each eld leaving this step towards the following step.
In the same way, if you select Show input elds, you will see the metadata of the data that
le the previous step.
Geng Started with Transformaons
[ 52 ]
The Select values step
The Select values step allows you to select, rename, and delete elds, or change the
metadata of a eld. The step has three tabs:
Select & Alter: This tab is also used to rename the elds or reorder them. This is
how we used it in the last exercise.
Remove: This tab is useful to discard undesirable elds. We used it in the matches
exercise to drop the rst and last elds. Alternavely, we could use the Select &
Alter tab, and specify the elds that you want to keep. Both are equivalent for
that purpose.
Meta-data: This tab is used when you want to change the denion of a eld such
as telling Kele to interpret a string eld as a date. We will see examples of this later
in this book.
You may use only one of the Select Values step tabs at a
me. Kele will not restrain you from lling more than one
tab, but that could lead to unexpected behavior.
Have a go hero – extending your transformations by writing output les
Suppose you read your own les in the previous secon, modify your transformaons by
wring some or all the data back into les, however, changing the format, headers, number
or order of elds, and so on this me around. The objecve is to get some experience to see
what happens. Aer some tests, you will feel condent with input and output les, and be
ready to move forward.
Getting system information
Unl now, you have learned how to read data from known les, and send data back to les.
What if you don't know beforehand the name of the le to process? There are several ways
to handle this with Kele. Let's learn the simplest.
Chapter 2
[ 53 ]
Time for action – updating a le with news about examinations
Imagine you are responsible to collect the results of an annual examinaon that is being
taken in a language school. The examinaon evaluates wring, reading, speaking, and
listening skills. Every professor gives the exam to the students, the students take the
examinaon, the professors grade the examinaons in the scale 0-100 for each skill, and
write the results in a text le, like the following:
80711-85;William Miller;81;83;80;90
20362-34;Jennifer Martin;87;76;70;80
75283-17;Margaret Wilson;99;94;90;80
83714-28;Helen Thomas;89;97;80;80
61666-55;Maria Thomas;88;77;70;80
All the les follow that paern.
When a professor has the le ready, he/she sends it to you, and you have to integrate the
results in a global list. Let's do it with Kele.
1. Before starng, be sure to have a le ready to read. Type it or download the sample les
from the Packt's ocial website.
2. Create the le where the news will be appended. Type this:
Annual Language Examinations
Testing writing, reading, speaking and listening skills
Save the le as C:/pdi_files/output/examination.txt.
3. Create a new transformaon.
4. Expand the Input branch of the steps tree.
5. Drag the Get System Info and Text le input icons to the canvas.
6. Expand the Output branch of the steps tree, and drag a Text le output step to
the canvas.
Geng Started with Transformaons
[ 54 ]
7. Link the steps as follows:
8. Double-click the rst Get System Info step icon and give it a name.
9. Fill the grid as follows:
10. Click OK.
11. Double-click the Text le Input step icon and congure it like here:
Chapter 2
[ 55 ]
12. Select the Content tab.
13. Check the Include lename in output? checkbox and type file_processed in the
Filename eldname textbox.
14. Check the Add lenames to result checkbox.
15. Select the Fields tab and Click the Get Fields buon to ll the grid.
16. Click OK.
17. Double-click the second Get System Info step icon and give it a name.
18. Add a eld named process_date, and from the list of choices select system
date (xed).
19. Double-click the Text le output step icon and give it a name.
20. Type C:/pdi_files/output/examination as the lename.
21. In the Fields tab, press the Get Fields buon to ll the grid.
22. Change the format of the Date row to yy/MM/dd.
23. Give a name and descripon to the transformaon and save it.
24. Press F9 to run the transformaon.
25. Fill in the argument grid, wring the full path of the le created.
26. Click Launch.
Geng Started with Transformaons
[ 56 ]
27. The output le should look like this:
Annual Language Examinations
Testing writing, reading, speaking and listening skills
80711-85;William Miller;81;83;80;90;C:\exams\exam1.txt;28-05-2009
20362-34;Jennifer Martin;87;76;70;80;C:\exams\exam1.txt;28-05-2009
75283-17;Margaret Wilson;99;94;90;80;C:\exams\exam1.txt;28-05-2009
83714-28;Helen Thomas;89;97;80;80;C:\exams\exam1.txt;28-05-2009
61666-55;Maria Thomas;88;77;70;80;C:\exams\exam1.txt;28-05-2009
28. Run the transformaon again.
29. This me ll the argument grid with the name of a second le.
30. Click Launch.
31. Verify that the data from this second le was appended to the previous data in the
output le.
What just happened?
You read a le whose name is known at runme, and fed a desnaon le by appending the
contents of the input le.
The rst Get System Info step tells Kele to take the rst command line argument, and
assume that it is the name of the le to read.
In the Text File Input step, you didn't specify the name of the le, but told Kele to take as
the name of the le, the eld coming from the previous step, which is the read argument.
With the second Get System Info step you just took from the system, the date, which you
used later to enrich the data sent to the desnaon le.
The desnaon le is appended with new data every me you run the transformaon.
Beyond the basic required data (student code and grades), the name of the processed le
and the date on which the data is being appended are added as part of the data.
When you don't specify the name and locaon of a le (like in this example), or
when the real le is not available at design me, you won't be able to use the
Get Fields buon, nor preview to see if the step is well congured. The trick is
to congure the step by using a real le idencal to the expected one. Aer the
step is congured, change the name and locaon of the le as needed.
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Getting information by using Get System Info step
The Get System Info step allows you to get dierent informaon from the system. In this
exercise, you took the system date and an argument. If you look to the available list, you
will see more than just these two opons.
Here we used the step in two dierent ways:
As a resource to take the name of the le from the command line
To add a eld to the dataset
The use of this step will be clearer with a picture.
In this example, the Text File Input doesn't know the name or the locaon of the le. It takes
it from the previous step, which is a Get System Info Step. As the Get System Info serves as
a supplier of informaon, the hop that leaves the step changes its look and feel to show
the situaon.
Geng Started with Transformaons
[ 58 ]
The second me the Get System Info is used, its funcon is simply to add a eld to the
incoming dataset.
Data types
Every eld must have a data type. The data type can be any of the common data
types—number (oat), string, date, Boolean, integer, or big number. Strings are simple,
just text for which you may specify a length. Date and numeric elds have more variants,
and are worthy of while a separate explanaon.
Date elds
Date is one the main data types available in Kele. In the matches tutorial, you have an
example of date eld—the match date eld. Its values were 2/Jun, 6/Jun, 10/Jun. Take a
look at how you dened that eld in the Text le input step. You dened the eld as a date
eld with format dd/MMM. What does it mean? To Kele it means that it has to interpret the
eld as a date, where the rst two posions represent the day, then there is a slash, and
nally there is the month in leers (that's the meaning of the three last posions).
Generally speaking, when a date eld is created, like the text input eld of the example, you
have to dene the format of the data so that Kele can recognize in the eld the dierent
components of the date. There are several formats that may be dened for a date, all of
them combinaons of leers that represents date or me components. Here are the most
basic ones:
Leers Meaning
M Month
d Day
H Hour (0-23)
m Minutes
s Seconds
Now let's see the other end of the same transformaon—the output step. Here you set
another format for the same eld: dd/MM. According the table, this means the date has to
have two posions for the day, then a slash, and then two posions for the month. Here, the
format specicaon represents the mask you want to apply when the date is shown. Instead
of 2/Jun, 6/Jun, 10/Jun, in the output le, you expect to see 02/06, 06/06, 10/06.
In the examinaon tutorial, you also have a Date eld—the process date. When you created
it, you didn't specify a format because you took the system date which, by denion, is a
date and Kele knows it. But when wring this date to the output le, again you dened a
format, in this case it was yyyy/MM/dd.
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In general, when you are wring a date, the format aribute is used of format the data
before sending it to the desnaon. In case you don't specify a format, Kele sets a
default format.
As said earlier, there are more combinaons to dene the format to a date eld.
For a complete reference, check the Sun Java API documentaon located at
Numeric elds
Numeric elds are present in almost all Kele transformaons. In the Examinaon example,
you encountered numeric elds for the rst me. The input le had four numeric elds.
As the numbers were all integer, you didn't set a specic format. When you have more
elaborate elds such as numbers with separators, dollar signs, and so on, you should specify
a format to tell Kele how to interpret the number. If you don't, Kele will do its best to
interpret the number, but this could lead to unexpected results.
At the other extreme of the ow, when wring to the output le text, you may specify the
format in which you want the number to be shown.
There are several formats you may apply to a numeric eld. The format is basically a
combinaon of predened symbols, each with a special meaning. The following are
the most used symbols:
Symbol Meaning
#Digit Leading zeros are not shown
0 Digit If the digit is not present, zero is displayed in its place
. Decimal separator
- Minus sign
%Field has to be mulplied by 100 and shown as a percentage
These symbols are not used alone. In order to specify the format of your numbers, you
have to combine them. Suppose that you have a numeric eld whose value is 99.55; the
following table shows you the same value aer applying dierent formats to it:
Format Result
# 100
0 100
#.# 99.6
#.## 99.55
#.000 99.550
000.000 099.550
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[ 60 ]
If you don't specify a format for your numbers, you may sll provide a Length and
Precision. Length is the total number of signicant gures, while precision is the number
of oang-point digits.
If you neither specify format nor length or precision, Kele behaves as follow. While reading,
it does its best to interpret the incoming number, and when wring, it sends the data as it
comes without applying any format.
For a complete reference on number formats, you can check the Sun Java API
documentaon available at
Running transformations from a terminal window
In the examinaon exercise, you specied that the name of the input le will be taken
from the rst command-line argument. That means when execung the transformaon,
the lename has to be supplied as an argument. Unl now, you only ran transformaons
from inside Spoon. In the last exercise, you provided the argument by typing it in a dialog
window. Now it is me to learn how to run transformaons with or without arguments from
a terminal window.
Time for action – running the examination transformation from
a terminal window
Before execung the transformaon from a terminal window, make sure that you have a new
examinaon le to process, let's say exam3.txt. Then follow these instrucons:
1. Open a terminal window and go to the directory where Kele is installed.
On Windows systems type:
C:\pdi-ce>pan.bat /file:c:\pdi_labs\examinations.ktr c:\
On Unix, Linux, and other Unix-based systems type:
/home/yourself/pdi-ce/ /file:/home/yourself/pdi_labs/
examinations.ktr c:/pdi_files/input/exam3.txt
If your transformaon is in another folder, modify the command
Chapter 2
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2. You will see how the transformaon runs, showing you the log in the terminal.
3. Check the output le. The contents of exam3.txt should be at the end of the le.
What just happened?
You executed a transformaon with Pan, the program that runs transformaons from
terminal windows. As part of the command, you specied the name of the transformaon
le and provided the name of the le to process, which was the only argument expected by
the transformaon. As a result, you got the same as if you had run the transformaon from
Spoon—a small le appended to the global le.
When you are designing transformaons, you run them with Spoon; you don't use Pan. Pan
is mainly used as part of batch processes, for example processes that run every night in a
scheduled fashion.
Appendix B tells you all the details about using Pan.
Have a go hero – using different date formats
Change the main transformaon of the last tutorial so that the process_date is saved with
a full format, that is, including day of week (Monday, Tuesday, and so on), month in leers
(January, February, and so on), and me.
Geng Started with Transformaons
[ 62 ]
Go for a hero – formatting 99.55
Create a transformaon to see for yourself the dierent formats for the number 99.55. Test
the formats shown in the Numeric elds secon and try some other opons as well.
To test this, you will need a dataset with a single row and a single eld—the
number. You can generate it with a Generate rows step.
Pop quiz–formatting data
Suppose that you read a le where the rst column is a numeric idener: 1, 2, 3, and so on.
You read the eld as a Number. Now you want to send the data back to a le. Despite being
a number, this eld is regular text to you because it is a code. How do you dene the eld in
the Text output step (you may choose more than one opon):
a. As a Number. In the format, you put #.
b. As a String. In the format, you put #.
c. As a String. You leave the format blank.
XML les
Even if you're not a system developer, you must have heard about XML les. XML les
or documents are not only used to store data, but also to exchange data between
heterogeneous systems over the Internet. PDI has many features that enable you to
manipulate XML les. In this secon you will learn to get data from those les.
Time for action – getting data from an XML le with information
about countries
In this tutorial you will build an Excel le with basic informaon about countries. The source
will be an XML le that you can download from the Packt website.
1. If you work under Windows, open the le located in the
C:/Documents and Settings/yourself/.kettle folder and add the
following line:
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[ 63 ]
On the other hand, if you work under Linux (or similar), open the kettle.
properties le located in the /home/yourself/.kettle folder and add the
following line:
2. Make sure that the directory specied in exists.
3. Save the le.
4. Restart Spoon.
5. Create a new transformaon.
6. Give a name to the transformaon and save it in the same directory you have all the
other transformaons.
7. From the Packt website, download the resources folder containing a file named
countries.xml. Save the folder in your working directory. For example, if your
transformaons are in pdi_labs, the le will be in pdi_labs/resources/.
The last two steps are important. Don't skip them! If you do,
some of the following steps will fail.
8. Take a look at the le. You can edit it with any text editor, or you can double-click it to
see it within an explorer. In any case, you will see informaon about countries. This is
just the extract for a single country:
<?xml version="1.0" encoding="UTF-8"?>
<capital>Buenos Aires</capital>
<language isofficial="T">
<language isofficial="F">
<language isofficial="F">
Geng Started with Transformaons
[ 64 ]
<name>Indian Languages</name>
9. From the Input steps, drag a Get data from XML step to the canvas.
10. Open the conguraon window for this step by double-clicking it.
11. In the File or directory textbox, press Ctrl+Space. A drop-down list appears as shown in
the next screenshot:
12. Select Internal.Transformation.Filename.Directory. The textbox gets lled
with this text.
13. Complete the text so that you can read ${Internal.Transformation.Filename.
14. Click on the Add buon. The full path is moved to the grid.
15. Select the Content tab and click Get XPath nodes.
16. In the list that appears, select /world/country/language.
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17. Select the Fields tab and ll the grid as follows:
18. Click Preview rows, and you should see something like this:
19. Click OK.
20. From the Output steps, drag an Excel Output step to the canvas.
21. Create a hop from the Get data from XML step to the Excel Output step.
22. Open the conguraon window for this step by double-clicking it.
Geng Started with Transformaons
[ 66 ]
23. In the Filename textbox press Ctrl+Space.
24. From the drop-down list, select ${LABSOUTPUT}.
25. By the side of that text type /countries_info. The complete text should be
26. Select the Fields tab and click the Get Fields buon to ll the grid.
27. Click OK. This is your nal transformaon.
28. Save the transformaon.
29. Run the transformaon.
30. Check that the countries_info.xls le has been created in the output directory
and contains the informaon you previewed in the input step.
What just happened?
You got informaon about countries from an XML le and saved it in a more readable
format—an Excel spreadsheet—for the common people.
To get the informaon, you used a Get data from XML step. As the source le was
taken from a folder relave to the folder where you stored the transformaon, you set
the directory to ${Internal.Transformation.Filename.Directory}. When
the transformaon ran, Kele replaced ${Internal.Transformation.Filename.
Directory} with the real path of the transformaon: c:/pdi_labs/.
In the same way, you didn't put a xed value for the path of the nal Excel le. As directory,
you used ${LABSOUTPUT}. When the transformaon ran, Kele replaced ${LABSOUTPUT}
with the value you wrote in the le. The output le was then saved in
that folder: c:/pdi_files/output.
Chapter 2
[ 67 ]
What is XML
XML stands for EXtensible Markup Language. It is basically a language designed to describe
data. XML les or documents contain informaon wrapped in tags. Look at this piece of XML
taken from the countries le:
<?xml version="1.0" encoding="UTF-8"?>
<capital>Buenos Aires</capital>
<language isofficial="T">
<language isofficial="F">
<language isofficial="F">
<name>Indian Languages</name>
The rst line in the document is the XML declaraon. It denes the XML version of the
document, and should always be present.
Below the declaraon is the body of the document. The body is a set of nested elements.
An element is a logical piece enclosed by a start-tag and a matching end-tag—for example,
<country> </country>.
Within the start-tag of an element, you may have aributes. An aribute is a markup
construct consisng of a name/value pair—for example, isofficial="F".
These are the most basic terminology related to XML les. If you want to know more about
XML, you can visit
Geng Started with Transformaons
[ 68 ]
PDI transformation les
Despite the .ktr extension, PDI transformaons are just XML les. As such, you are able to
explore them inside and recognize dierent XML elements. Look the following sample text:
<?xml version="1.0" encoding="UTF-8"?>
<description>My first transformation</description>
This transformation generates 10 rows
with the message Hello World.
This is an extract from the hello_world.ktr le. Here you can see the root element
named transformation, and some inner elements such as info and name.
Note that if you copy a step by selecng it in the Spoon canvas and pressing Ctrl+C , and then
pass it to a text editor, you can see its XML denion. If you copy it back to the canvas, a new
idencal step will be added to your transformaon.
Getting data from XML les
In order to get data from an XML le, you have to use the Get Data From XML input step.
To tell PDI which informaon to get from the le, it is required that you use a parcular
notaon named XPath.
XPath is a set of rules used for geng informaon from an XML document. In XPath, XML
documents are treated as trees of nodes. There are several types of nodes; elements,
aributes, and texts are some of them. As an example, world, country, and isofficial
are some of the nodes in the sample le.
Among the nodes there are relaonships. A node has a parent, zero or more children,
siblings, ancestors, and descendants depending on where the other nodes are in
the hierarchy.
In the sample countries le, country is the the parent of the elements name, capital, and
language. These three elements are children of country.
To select a node in an XML document, you have to use a path expression relave to a
current node.
Chapter 2
[ 69 ]
The following table has some examples of path expressions that you may use to specify
elds. The examples assume that the current node is language.
Path expression Descripon Sample expression
node_name Selects all child nodes of the
node named node_name.
This expression selects all child nodes of
the node percentage. It looks for the node
percentage inside the current node language.
.Selects the current node language
.. Selects the parent of the
current node
This expression selects all child nodes of the
node capital. It doesn't look in the current
node (language), but inside its parent, which
is country.
@Selects an aribute @isofficial
This expression gets the aribute isofficial
in the current node language.
Note that the expressions name and ../name are not the same. The
rst selects the name of the language, while the second selects the
name of the country.
For more informaon on XPath, follow this link:
Conguring the Get data from XML step
In order to specify the name and locaon of an XML le, you have to ll the File tab just as
you do in any le input step. What is dierent here is how you get the data.
The rst thing you have to do is select the path that will idenfy the current node. You do
it by lling the Loop XPath textbox in the Content tab. You can type it by hand, or you can
select it from the list of available paths by Clicking the Get XPath nodes buon.
Once you have selected a path, PDI will generate one row of data for every found path.
In the tutorial you selected /world/country/language. Then PDI generates one row for
each /world/country/language element in the le.
Aer selecng the loop XPath, you have to specify the elds to get. In order to do that,
you have to ll the grid in the Fields tab by using XPath notaon as explained in the
preceding secon.
Geng Started with Transformaons
[ 70 ]
Note that if you click the Get elds buon, PDI will ll the grid with the child nodes of the
current node. If you want to get some other node, you have to type its XPath by hand.
Also note the notaon for the aributes. To get an aribute, you can use the @ notaon as
explained, or you can simply type the name of the aribute without @ and select Aribute
under the Element column, as you did in the tutorial.
Kettle variables
In the last tutorial, you used the string ${Internal.Transformation.Filename.
Directory} to idenfy the folder where the current transformaon was saved. You also
used the string ${LABSOUTPUT} to dene the desnaon folder of the output le.
Both strings, ${Internal.Transformation.Filename.Directory} and
${LABSOUTPUT}, are Kele variables, that is, keywords linked to a value. You use the
name of a variable, and when the transformaon runs, the name of the variable is
replaced by its value.
The rst of these two variables is an environment variable, and it is not the only available.
Other known environment variables are ${user.home}, ${}, and
${java.home}. All these variables are ready to use any me you need.
The second variable is a variable you dened in the le. In this le
you may dene as many variables as you want. The only thing you have to keep in mind is
that those variables will be available inside Spoon aer you restart it.
These two kinds of variables—environment variables and variables dened in the le—are the most primive kinds of variables found in PDI.
All of these variables are string variables and their scope is the Java virtual machine.
How and when you can use variables
Any me you see a red dollar sign by the side of a textbox, you may use a variable. Inside the
textbox you can mix variable names with stac text, as you did in the tutorial when you put
the name of the desnaon as ${LABSOUTPUT}/countries_info.
To see all the available variables, you have to posion the cursor in the textbox, press
Ctrl+Space, and a full list is displayed for you to select the variable of your choice. If you put
the mouse cursor over any of the variables for a second, the actual value of the variable will
be shown.
If you know the name of the variable, you don't need to select it from the list. You may type
its name, by using either of these notaons—${<name>} or %%<name>%%.
Chapter 2
[ 71 ]
Have a go hero – exploring XML les
Now you can explore by yourself. On the Packt website there are some sample XML les.
Download them and try this:
• Read the customer.xml le and create a list of customers.
• Read the tomcat-users.xml le and get the users and their passwords.
• Read the areachart.xml and get the color palee, that is, the list of colors used.
The customer le is included in the Pentaho Report Designer soware package.
The others come with the Pentaho BI package. This soware has many XML les
for you to use. If you are interested you can download the soware from
Have a go hero – enhancing the output countries le
Modify the transformaon in the tutorial so that the Excel output uses a template. The
template will be an Excel le with the header and format already applied, and will be located
in a folder inside the pdi_labs folder.
Templates are congured in the Content tab of the Excel conguraon window.
In order to set the name for the template, use internal variables.
Have a go hero – documenting your work
As explained, transformaons are nothing dierent than XML les. Now you'll create a new
transformaon that will take as input the transformaons you've created so far, and will
create a simple Excel spreadsheet with the name and descripon of all your transformaons.
If you keep this sheet updated by running the transformaon on a regular basis, it will be
easier to nd a parcular transformaon you created in the past.
To get data from the transformaons les, use the Get data from XML step.
As wildcard, use .*\.ktr. Doing so, you'll get all the les.
On the other hand, as Loop XPath, use /transformation/info.
Geng Started with Transformaons
[ 72 ]
In this chapter you learned how to get data from les and put data back into les.
Specically, you learned how to:
Get data from plain les and also from XML les
Put data into text les and Excel les
Get informaon from the operang system such as command-line arguments and
system date
We also discussed the following:
The main PDI terminology related to data, for example datasets, data types,
and streams
The Select values step, a commonly used step for selecng, reordering, removing
and changing data
How and when to use Kele variables
How to run transformaons from a terminal with the Pan command
Now that you know how to get data into a transformaon, you are ready to start
manipulang data. This is going to happen in the next chapter.
Basic Data Manipulation
In the previous chapter, you learned how to get data into PDI. Now you're ready to
begin transforming that data. This chapter explains the simplest and most used ways
of transforming data. We will cover the following:
Execung basic operaons
Filtering and sorng of data
Looking up data outside the main stream of data
By the end of this chapter, you will be able to do simple but meaningful transformaons on
dierent types of data.
Basic calculations
You already know how to create a transformaon and read data from an external source.
Now, taking that data as a starng point, you will begin to do basic calculaons.
Basic Data Manipulaon
[ 74 ]
Time for action – reviewing examinations by using the
Calculator step
Can you recollect the exercise about examinaons you did in the previous chapter? You
created an incremental le with examinaon results. The nal le looked like the following:
Annual Language Examinations
Testing writing, reading, speaking and listening skills
80711-85;William Miller; 81;83;80;90;C:\pdi_files\input\first_turn.
20362-34;Jennifer Martin; 87;76;70;80;C:\pdi_files\input\first_turn.
75283-17;Margaret Wilson; 99;94;90;80;C:\pdi_files\input\first_turn.
83714-28;Helen Thomas; 89;97;80;80;C:\pdi_files\input\first_turn.
61666-55;Maria Thomas; 88;77;70;80;C:\pdi_files\input\first_turn.
Now you want to convert all grades in the scale 0-100 to a new scale from 0 to 5. Also, you
want to take the average grade to see how the students did.
1. Create a new transformaon, give it a name and descripon, and save it.
2. By using a Text le input step, read the examination.txt le. Give the name and
locaon of the le, check the Content tab to see that everything matches your le, and
ll the Fields tab as here:
Chapter 3
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3. Do a preview just to conrm that the step is well congured.
Noce that you have several lines as header. Because the
names of the elds are not in the rst row, you won't be able
to use the Get Fields buon successfully. You will have to write
the elds manually, or you can avoid it by doing the following:
Congure the step with a copy of the le that doesn't have the
extra heading, just the heading row with the names of the elds.
Then, restore the name of your le in the File tab, adjust the
number of headings in the Content tab, and your step is ready.
4. Use the Select values step to remove the elds you will not use—file_processed
and process_date.
Basic Data Manipulaon
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5. Drag another Select values step to the canvas. Select the Meta-data tab and change the
meta-data of the numeric elds like here:
6. Near the upper-le corner of the screen, above the step tree, there is a textbox for
searching. Type calc in the textbox. While you type, a lter is applied to show you only
the steps that contain, in their name or descripon, the text you typed. You should be
seeing this:
7. Among the steps you see, select the Calculator step and drag it to the canvas.
Chapter 3
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8. To remove the lter, clear the typed text.
9. Create a hop from the Text le input step to the Calculator step.
10. Edit the Calculator step and ll the grid as follows:
11. To ll the Calculaon column, simply select the operaon from the list provided. Be sure
to ll every column in the grid like shown in the screenshot.
You don't have to feel like you are doing data entry instead
of learning PDI. You can avoid typing by copying and pasng
similar rows, and then xing the values properly. Appendix D
has a list of shortcuts you can use when eding grids like these.
12. Leave the Calculator step selected and click the Preview this transformaon buon
followed by the Quick Launch buon. You should see something similar to the
following screenshot:
Basic Data Manipulaon
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The numbers may vary according to the contents of your le.
13. Edit the calculator again and change the content of the Remove column like here:
14. From the Transform category of steps, add a Sort rows step and create a hop from the
Calculator step to this new step.
15. Edit the Sort rows step by double-clicking it, click the Get Fields buon, and adjust the
grid as follows:
16. Click OK.
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17. Drag a third Select values step, create a hop from the Sort rows step to this new step,
and use it to keep only the elds by which you ordered the data:
18. From the Flow category of steps, add a Dummy step and create a hop from the last
Select values step to this.
19. Select the Dummy step and do a preview.
20. The nal preview looks like the following screenshot:
Basic Data Manipulaon
[ 80 ]
If you get an error or a dierent result, review the explanaon and make
sure that you followed the instrucons correctly. Do a preview on each
step to discover in which one you have the problem. If you realize that
the problem is in any of the steps that read the input les, please refer
to the Troubleshoong reading les secon in Chapter 2.
What just happened?
You read the examination.txt le, and did some calculaons to see how the students did.
You did the calculaons by using the Calculator step.
First of all, you removed the elds you didn't need from the stream of data.
Aer that, you did the following calculaons:
By dividing by 20, you converted all grades from the scale 0-100 to the scale 0-5.
Then, you calculated the average of the grades for the four skills—wring, reading, listening,
and speaking. You created two auxiliary elds, aux1 and aux2, to calculate paral sums. Aer
that, you created the eld total with the sum of aux1 and aux2, another auxiliary eld with
the number 4, and nally the avg as the division of the total by the eld four.
In order to obtain the new grades, as well as the average with two decimal posions, you
need the result of the operaon to be of a numeric type with precision 2. Therefore, you
had to change the metadata, by adding a Select values step before the Calculator. With the
Select values you changed the type of the numeric elds from integer to number, that is,
oat numbers. If you didn't, the quoents would have been rounded to integer numbers.
You can try and see for yourself!
The rst me you edited the calculator, you set the eld Remove to N for every row in the
calculator grid. By doing this, you could preview every eld created in the calculator, even
the auxiliary ones such as the elds twenty, aux1, and aux2. You then changed the eld to
Y so that the auxiliary elds didn't pass to the next step.
Aer doing the calculaons, you sorted the data by using a Sort rows step. You specied the
order by avg descending, then by student_code ascending.
Chapter 3
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Sorng data
For small datasets, the sorng algorithm runs mainly using the JVM memory.
When the number of rows exceeds 5,000, it works dierently. Every ve
thousand rows, the process sorts them and writes them to a temporary le.
When there are no more rows, it does a merge sort on all those les and gives
you back the sorted dataset. You can conclude that for huge datasets a lot
of reading and wring operaons are done on your disk, which slows down
the whole transformaon. Fortunately, you can change the number of rows
in memory (5,000 by default) by seng a new value in the Sort size (rows in
memory) textbox. The bigger this number, the faster the sorng process.
Note that a sort size that works in your system may not work in a machine with
a dierent conguraon. To avoid that risk, you can use a dierent approach.
In the Sort rows conguraon window, you can set a Free memory threshold
(in %) value. The process begins to use temporary les when the percentage
of available memory drops below the indicated threshold. The lower the
percentage, the faster the process.
As it's not possible to know the exact amount of free memory, it's not
recommended to set a very small free memory threshold. You denitely
shouldn't use that opon in complex transformaons or when there is more
than one sort going on, as you could sll run out of memory.
The two nal steps were added to keep only the elds of interest, and to preview the result
of the transformaon. You can change the Dummy step for any of the output steps you
already know.
You've used the Dummy step several mes but sll nothing has been said
about it. Mainly it was because it does nothing! However, you can use it as a
placeholder for tesng purposes as in the last exercise.
Note that in this tutorial you used the Select values step in three dierent ways:
To remove elds by using the Remove tab.
To change the meta-data of some elds by using the Meta-data tab.
To select and rename elds by using the Select tab.
Remember that the Select values step's tabs are exclusive! You can't use more
than one in the same step!
Basic Data Manipulaon
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Besides calculaon, in this tutorial you did something you hadn't before—searching the
step tree.
When you don't remember where a step is in the steps tree, or when you just
want to nd if there is a step that does some kind of operaon, you could simply
type the search criterion in the textbox above the steps tree. PDI does a search
and lters all the steps that have that text as part of their name or descripon.
Adding or modifying elds by using different PDI steps
In this tutorial you used the Calculator step to create new elds and add them to
your dataset. The Calculator is one the many steps that PDI has to create new elds by
combining existent ones. Usually you will nd these steps under the Transform category
of the steps tree. The following table describes some of them (the examples refer to the
examinaon le):
Step Descripon Example
Split Fields Split a single eld into two
or more. You have to give
the character that acts as
Split the name into two elds: Name and
Last Name. The separator would be a space
Add constants Add one or more constants
to the input rows
Add two constants: four and twenty. Then
you could use them in the Calculator step
without dening the auxiliary elds.
Replace in string Replace all occurrences of
a text in a string eld with
another text
Replace the in the student code by a /.
For example: 108418-95 would become
Number range Create a new eld based on
ranges of values. Applies to
a numeric eld.
Create a new eld called exam_range with
two ranges: Range A with the students with
average grade below 3.5, and Range B with
students with average grade greater or equal
to 3.5.
Value Mapper Creates a correspondence
between the values of
a eld and a new set of
Suppose you calculated the average grade as
an integer number ranging from 0 to 5. You can
map the average to A, B, C, D, like this:
Old value: 5; New value: A
Old value: 3, 4; New value: B
Old value: 1, 2; New value: C
Old value: 0; New value: D
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Step Descripon Example
User Dened Java
Creates a new eld by
using a Java expression that
involves one or more elds.
This step may eventually
replace any of the above
but it's only recommended
for those familiar with Java.
Create a ag (a Boolean eld) that tells if a
student passed. A student passes if his/her
average grade is above 4.5.
The expression to use could be:
Any of these steps when added to your transformaon, are executed for every row in the
stream. It takes the row, idenes the elds needed to do its tasks, calculates the new
eld(s), and adds it to the dataset.
For details on a parcular step, don't hesitate to visit the Wiki page for steps:
The Calculator step
The Calculator step you used in the tutorial, allows you to do simple calculaons not only
on numeric elds, but also on data and text. The Calculator step is not the only means to do
calculaons, but it is the simplest. It allows you to do simple calculaons in a quick fashion.
The step has a grid where you can add all the elds you want to. Every row represents
an operaon that involves from one up to three operands (depending on the selected
operaon). When you select an operaon, the descripon of the operaon itself tells you
which argument it needs. For example:
If you select Set constant eld to value A, you have to provide a constant value
under the column name A.
If you select A/B, the operaon needs two arguments, and you have to provide
them by indicang the elds to use in the columns named A and B respecvely.
The result of every operaon becomes a new eld in your dataset, unless you set the
Remove column to Y. The name of the new eld is the one you type under the New
eld column.
For each and every row of the data set, the operaons dened in the Calculator are
calculated in the order in which they appear. Therefore, you may create auxiliary elds and
then use them in rows of the Calculator grid that are below them. That is what you did in
the tutorial when you dened the auxiliary elds aux1 and aux2 and then used them in the
eld total.
Basic Data Manipulaon
[ 84 ]
Just like every grid in Kele, you have a contextual menu (and its corresponding shortcuts)
that lets you manipulate the rows by deleng, moving, copying and pasng, and so on.
The Formula step
The Formula step is another step you can use for doing calculaons. Let's give it a try by
using it in the examinaon tutorial.
Time for action – reviewing examinations by using the
Formula step
In this tutorial you will redo the previous exercise, but this me you will do the calculaons
with the Formula step.
1. Open the transformaon you just nished.
2. Delete from the transformaon the Calculator step, and put in its place a Formula
step. You will nd it under the Scripng category of steps.
3. Add a eld named writing.
4. When you click the cell under the Formula column, a window appears to edit the
formula for the new eld.
5. In the upper area of the window, type [writing]/20. You will noce that the
sentence is red if it is incomplete or the syntax is incorrect. In that case, the error is
shown below the eding area, like in the following example:
Chapter 3
[ 85 ]
6. As soon as the formula is complete and correct, the red color disappears.
7. Click OK.
8. The formula you typed will be displayed in the cell you clicked.
9. Set Number as the type for the new eld, and type writing in the Replace value
10. Add three more elds to the grid in the same way you added this eld so that the
grid looks like the following:
11. Click OK.
12. Add a second Formula step.
13. Add a eld named avg and click the Formula cell to edit it.
14. Expand the Mathemacal category of funcons to the leside of the window, and click
the AVERAGE funcon.
Basic Data Manipulaon
[ 86 ]
15. The explanaon of the selected funcon appears to guide you.
16. In the eding area, type average([writing];[reading];[speaking];
17. Click OK.
18. Set the Value type to Number.
19. Click OK.
20. Create a hop from this step to the Sort rows step.
21. Edit the last Select values step.
22. Click Get elds to select.
23. A queson appears to ask you what to do. Click Clear and add all.
24. The grid is reloaded with the modied elds.
25. Click on the Dummy step and do a preview.
26. There should be no dierence with what you had in the Calculator version of
the tutorial:
Chapter 3
[ 87 ]
What just happened?
You read the examination.txt le, and did some calculaons using the Formula step to
see how the students did.
It may happen that the preview window shows you less decimal posions than
expected. This is a preview issue. One of the ways you have to see the numbers
with more decimals is to send the numbers to an output le with a proper
format and see the numbers in the le.
As you saw, you have quite a lot of funcons available for building formulas and expressions.
To reference a eld you have to use square brackets, like in [writing]. You may reference
only the current elds of the row. You have no way to access previous rows of the grid as
you have in the Calculator step and so you needed two Formula steps to replace a single
Calculator. But you saved auxiliary elds because the Formula allows you to type complex
formulas in a single eld without using paral calculaons.
When the calculaons are not simple, that is, they require resolving a complex
formula or involve many operands, then you might prefer the Formula step over
the Calculator.
The Formula step uses the library Libformula. The syntax used in LibFormula is based
on the OpenFormula standard. For more informaon on OpenFormula, you may visit
Basic Data Manipulaon
[ 88 ]
Have a go hero – listing students and their examinations results
Let's play a lile with the examinaon le. Suppose you decide that only those students
whose average grade was above 3.9 will pass the examinaon; the others will not. List the
students ordered by average (desc.), last name (asc.), and name (asc.). The output list should
have the following elds:
Student code
Last Name
Passed (yes/no)
average grade
Pop quiz – concatenating strings
Suppose that you want to create a new eld as the student_code plus the name of the
student separated by a space, as for example 867432-94 Linda Rodriguez. Which of the
following are possible soluons for your problem:
a. Use a Calculator, using the calculaon a+b+c, where a is student_code, b is a
space, and c is the name eld.
b. Use a Formula, using as formula [student_code]+" "+[name]
c. Use a Formula, using as formula [student_code]&" "&[name]
You may choose more than one opon.
Calculations on groups of rows
You just learned to do simple operaons for every row of a dataset. Now you are ready to
go beyond. Suppose you have a list of daily temperatures of a given country over a year. You
may want to know the overall average temperature, the average temperature by region,
or the coldest day of the year. When you work with data, these types of calculaons are a
common requirement. In this secon you will learn to address those requirements with PDI.
Chapter 3
[ 89 ]
Time for action – calculating World Cup statistics by
grouping data
Let's forget the examinaons for a while, and retake the World Cup tutorial from the
previous chapter. The le you obtained from that tutorial was a list of results of football
matches. These are sample rows of the nal le:
Match Date;Home Team;Away Team;Result
Now you want to take that informaon to obtain some stascs such as the maximum
number of goals per match in a given day. To do it, follow these instrucons:
1. Create a new transformaon, give it a name and descripon, and save it.
2. By using a Text le input step, read the wcup_first_round.txt le you generated
in Chapter 2. Give the name and locaon of the le, check the Content tab to see that
everything matches your le, and ll the Fields tab.
3. Do a preview just to conrm that the step is well congured.
4. From the Transform category of step, select a Split Fields step, drag it to the work area,
and create a hop from the Text le input to this step.
5. Double-click the Split Fields steps and ll the grid like done in the following screenshot:
Basic Data Manipulaon
[ 90 ]
6. Add a Calculator step to the transformaon and create a hop from the Split Fields step
to this step and edit the step to create the following new elds:
7. Add a Sort rows step to the transformaon, create a hop from the Calculator step to this
step, and sort the elds by Match_Date.
8. Expand the Stascs category of steps, and drag a Group by step to the canvas. Create a
hop from the Sort rows step to this new step.
9. Edit the Group by step and ll the conguraon window as shown next:
Chapter 3
[ 91 ]
10. When you click the OK buon, a window appears to warn you that this step
needs the input to be sorted on the specied keys—the Range eld in this case.
Click I understand, and don't worry because you already sorted the data in the
previous step.
11. Add a nal Dummy step.
12. Select the Dummy and the Group by steps, le-click one and holding down the Shi
key, le-click the other.
13. Click the Preview this transformaon buon. You will see the the following:
14. Click Quick Launch. The following window appears:
15. Double-click the Sort rows step. A window appears with the data coming out of the Sort
rows step.
16. Double-click the Dummy step. A window appears with the data coming out of the
Dummy step.
Basic Data Manipulaon
[ 92 ]
17. If you rearrange the preview windows, you can see both preview windows at a me, and
understand beer what happened with the numbers. The following would be the data
shown in the windows:
What just happened?
You opened a le with results from several matches and got some stascs from it.
In the le, there was a column with the match result in the format n-m, with n being the
goals of the home team and m being the goals of the away team. With the Split Fields step,
you split this eld in two—one with each of these two numbers.
With the Calculator you did two things:
You created a new eld with the total number of goals for each match.
You created a descripon for the match.
Chapter 3
[ 93 ]
Note that in order to create a descripon, you used the + operator to
concatenate string rather than add numbers.
Aer that, you ordered the data by match date with a Sort rows step.
In the preview window of the Sort rows step, you could see all the calculated elds: home
team goals, away team goals, match goals, and descripon.
Finally, you did some stascal calculaons:
First, you grouped the rows by match date. You did this by typing Match_Date in the
upper grid of the Group by step.
Then, for every match date, you calculated some stascs. You did the calculaons by
adding rows in the lower grid of the step, one for every stasc you needed.
Let's see how it works. Because the Group by step was preceded by a Sort rows step, the
rows came to the step already ordered. When the rows arrive to the Group by step, Kele
creates groups based on the eld(s) indicated in the upper grid—the Match_Date eld in this
case. The following drawing shows this idea:
Basic Data Manipulaon
[ 94 ]
Then, for every group, the elds that you put in the lower grid are calculated. Let's see, for
example, the group for the match date 03/06. For the rows in this group, Kele calculated
the following:
Matches: The number of matches played on 03/06. There were 4.
Sum of goals: The total number of goals converted on 03/06. There were 3+2+3+4=12.
Maximum: The maximum number of goals converted in a single match played on 03/06.
The maximum among 3, 2, 3, and 4 was 4.
Teams: The descripons of the teams which played on 03/06, separated by ; : Austria-
Spain; Sweden-Brazil; Netherlands-Iran; Peru-Scotland.
The same calculaons were made for every group. You can verify the details by looking in the
preview window.
Look at the Step Metrics tab in the Execuon Results area of the screen:
Note that 24 rows entered the Group by step and only 7 came out of that step towards the
Dummy step. That is because aer the grouping, you no longer have the detail of matches.
The output of the Group by step is your new data now—one row for every group created.
Group by step
The Group by step allows you to create groups of rows and calculate new elds over
those groups.
In order to dene the groups, you have to specify which eld(s) are the keys. For every
combinaon of values for those elds, Kele builds a new group.
In the tutorial you grouped by a single eld Match_date. Then for every value of
Match_date, Kele created a dierent group.
Chapter 3
[ 95 ]
The Group by step operates on consecuve rows. Suppose that the rows are already sorted
by date, but those with date 10/06 are above the rest. The step traverses the dataset and
each me the value for any of the grouping eld changes, it creates a new group. If you
see it this way, you will noce that the step will work even if the data is not sorted by the
grouping eld.
As you probably don't know how the data is ordered, it is safer and
recommended that you sort the data by using a Sort rows step just
before using a Group by step.
Once you have dened the groups, you are free to specify new elds to be calculated
for every group. Every new eld is dened as an aggregate funcon over some of the
existent elds.
Let's review some of the elds you created in the tutorial:
The Matches eld is the result of applying the Number of values funcon over
the eld Match_date.
The Sum of goals eld is the result of applying the Sum funcon over the
eld goals.
The Maximum eld is the result of applying the Maximum funcon over the
eld goals.
Finally, you have the opon to calculate aggregate funcons over the whole dataset. You do
this by leaving the upper grid blank. Following the same example, you could calculate the
total number of matches and the average number of goals for all those matches. This is how
you do it:
Basic Data Manipulaon
[ 96 ]
The following is what you get:
In any case, as a result of the Group by step, you will no longer have the detailed rows,
unless you check the Include all rows? checkbox.
Have a go hero – calculating statistics for the examinations
Here you have one more task related with the examinaons le. Create a new
transformaon, read the le, and calculate:
The number of students who passed
The number of students who failed
The average wring, reading, speaking, and listening grade obtained by students
who passed
The average wring, reading, speaking, and listening grade obtained by students
who failed
The minimum and maximum average grade among students who passed
The minimum and maximum average grade among students who failed
Use the Number range step to dene the range of the average
grade; then use a Group by step to calculate the stascs.
Chapter 3
[ 97 ]
Have a go hero – listing the languages spoken by country
Read the le with countries' informaon you used in Chapter 2. Build a le where each row
has two columns—the name of a country and the list of spoken languages in that country.
As aggregate, use the opon Concatenate strings separated by.
Unl now you learned how to accomplish several kinds of calculaons that enriched the set
of data. There is sll another kind of operaon that is frequently used, and does not have to
do with enriching the data but with discarding data. It is ltering unwanted data. Now you
will learn how to discard rows under given condions.
Time for action – counting frequent words by ltering
Let's suppose, you have some plain text les, and you want to know what is said in them. You
don't want to read them, so you decide to count the mes that words appear in the text, and
see the most frequent ones to get an idea of what the les are about.
Before starng, you'll need at least one text le to play with. The text le used in
this tutorial is named smcng10.txt and is available for you to download from
the Packt website.
Let's work:
1. Create a new transformaon.
2. By using a Text le input step, read your le. The trick here is to put as a separator
a sign you are not expecng in the le, for example |. By doing so, the enre line
would be recognized as a single eld. Congure the Fields tab by dening a single
string eld named line.
3. From the Transform category of step, drag to the canvas a Split eld to rows step,
and create a hop from Text le input step to this new step.
Basic Data Manipulaon
[ 98 ]
4. Congure the step like this:
5. With this last step selected, do a preview. Your preview window should look like this:
6. Close the preview window.
7. Expand the Flow category of steps, and drag a Filter rows step to the work area.
8. Create a hop from the last step to the Filter rows step.
9. Edit the Filter rows step by double-clicking it.
Chapter 3
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10. Click the <field> textbox to the le of the = sign. The list of elds appears.
Select word.
11. Click the = sign. A list of operaons appears. Select IS NOT NULL.
12. The window looks like the following:
13. Click OK.
14. From the Transform category of steps drag a Sort rows step to the canvas, and
create a hop from the Filter rows step to this new step.
15. Sort the rows by word.
16. From the Stascs category, drag a Group by step, and create a hop from the Sort
rows step to this step.
17. Congure the grids in the Group by conguraon window like shown:
Basic Data Manipulaon
[ 100 ]
18. Add a Calculator step, create a hop from the last step to this, and calculate the new
eld len_word represenng the length of the words. For that, use the calculator
funcon Return the length of a string A and select word from the
drop-down menu for Field A.
19. Expand the Flow category and drag another Filter rows step to the canvas.
20. Create a hop from the Calculator step to this step and edit it.
21. Click <field> and select counter.
22. Click the = sign, and select >.
23. Click <value>. A small window appears.
24. In the Value textbox of the lile window, enter 2.
25. Click OK.
26. Posion the mouse cursor over the icon in the upper-right corner of the window.
When the text Add condion shows up, click on the icon.
27. A new blank condion is shown below the one you created.
28. Click on null = [] and create the condion len_word>3, in the same way you
created the condion counter>2.
29. Click OK.
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30. The nal condion looks like this:
31. Add one more Filter rows step to the transformaon and create a hop from the last
step to this new step.
32. On the le side of the condion, select word.
33. As comparator select IN LIST.
34. At the end of the condion, inside the textbox value, type the following:
35. Click the upper-le square above the condion and the word NOT will appear.
36. The condion looks like the following:
Basic Data Manipulaon
[ 102 ]
37. Add a Sort rows step, create a hop from the previous step to this step, and sort the
rows in the descending order of counter.
38. Add a Dummy step at the end of the transformaon, create a hop from the last step
to the Dummy step.
39. With the Dummy step selected, preview the transformaon. The following is what
you should see now:
What just happened?
You read a regular plain le and arranged the words that appear in the le in some
parcular fashion.
The rst thing you did was to read the plain le and split the lines so that every word became
a new row in the dataset. Consider, for example, the following line:
subsidence; comparison with the Portillo chain.
Chapter 3
[ 103 ]
The spling of this line resulted in the following rows being generated:
Thus, a new eld named word became the basis for your transformaon.
First of all, you discarded rows with null words. You did it by using a lter with the condion
word IS NOT NULL. Then, you counted the words by using the Group by step you learned
in the previous tutorial. Once you counted the words, you discarded those rows where the
word was too short (length less than 4) or too common (comparing to a list you typed).
Once you applied all those lters, you sorted the rows in the descending order of
the number of mes the word appeared in the le so that you could see the most
frequent words.
Scrolling down a lile the preview window to skip some preposions, pronouns, and other
very common words that have nothing to do with a specic subject, you found words such
as shells, strata, formaon, South, elevaon, porphyric, Valley, terary, calcareous, plain,
North, rocks, and so on. If you had to guess, you would say that this was a book or arcle
about geology, and you would be right. The text taken for this exercise was Geological
Observaons on South America by Charles Darwin.
Filtering rows using the Filter rows step
The Filter rows step allows you to lter rows based on condions and comparisons.
The step checks the condion for every row. Then it applies a lter leng pass only the rows
for which the condion is true. The other rows are lost.
In the counng words exercise, you used the Filter rows step several mes so you already
have an idea of how it works. Let's review it.
Basic Data Manipulaon
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In the Filter rows seng window you have to enter a condion. The following table
summarizes the dierent kinds of condions you may enter:
Condion Descripon Example
A single eld followed by IS NULL or
Checks whether the value of a
eld in the stream is null
A eld, a comparator, and a constant Compares a eld in the stream
against a constant value.
counter > 2
Two elds separated by a comparator Compares two elds in the
You can combine condions as shown here:
counter > 2
You can also create subcondions such as:
counter > 2
(word in list geology; sun)
In this last example, the condion lets the word geology pass even if it appears only once. It
also lets the word sun pass, despite its length.
When eding condions, you always have a contextual menu which allows you to add and
delete sub-condions, change the order of existent condions, and more.
Maybe you wonder what the Send 'true' data to step: and Send 'false' data to step: textboxes
are for. Be paent, you will learn how to use them in Chapter 4.
Have a go hero – playing with lters
Now it is your turn to try ltering rows. Modify the counng_words transformaon in the
following way:
Alter the Filter rows steps. By using a Formula step create a ag (a Boolean eld)
that evaluates the dierent condions (counter>2, and so on). Then use only one
Filter rows step that lters the rows for which the ag is true. Test it and verify that
the results are the same as before the change.
Chapter 3
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In the Formula eding window, use the opons under the Logic category.
Then in the Filter rows step, you can type true or Y as the value against which
you compare the ag.
Add a sub-condion to avoid excluding some words, just like the one in the example:
(word in list geology; sun). Change the list of words and test the lter to see
that the results are as expected.
Have a go hero – counting words and discarding those that are
commonly used
If you take a look at the results in the tutorial, you may noce that some words appear more
than once in the nal list because of special signs such as . , ) or ", or because of lower
or upper case leers. For example, look how many mes the word rock appears: rock (99
occurrences) - rock,(51 occurrences) – rock. (11 occurrences) – rock." (1 occurrence)
- rock: (6 occurrences) - rock; - (2 occurrences). You can x this and make the word rock
appear only once: Before grouping the words, remove all extra signs and convert all words to
lower case or upper case, so they are grouped as expected.
Try one or more of the following steps: Formula, Calculator, Replace in string.
Looking up data
Unl now, you have been working with a single stream of data. When you did calculaons or
created condions to compare elds, you only involved elds of your stream. Usually, this is
not enough, and you need data from other sources. In this secon you will learn to look up
data outside your stream.
Time for action – nding out which language people speak
An Internaonal Musical Contest will take place and 24 countries will parcipate, each
presenng a duet. Your task is to hire interpreters so the contestants can communicate in
their nave language. In order to do that, you need to nd out the language they speak:
1. Create a new transformaon.
2. By using a Get Data From XML step, read the countries.xml le that contains
informaon about countries that you used in Chapter 2.
Basic Data Manipulaon
[ 106 ]
To avoid conguring the step again, you can open the transformaon
that reads this le, copy the Get data from XML step, and paste it here.
3. Drag a Filter rows step to the canvas.
4. Create a hop from the Get data from XML step to the Filter rows step.
5. Edit the Filter rows step and create the condion- isofficial= T.
6. Click the Filter rows step and do a preview. The list of previewed rows will show the
countries along with the ocial languages:
Now let's create the main ow of data:
7. From the book website download the list of contestants. It looks like this:
1;Russia;Mikhail Davydova
;;Anastasia Davydova
2;Spain;Carmen Rodriguez
;;Francisco Delgado
3;Japan;Natsuki Harada
;;Emiko Suzuki
4;China;Lin Jiang
;;Wei Chiu
5;United States;Chelsea Thompson
;;Cassandra Sullivan
6;Canada;Mackenzie Martin
;;Nathan Gauthier
7;Italy;Giovanni Lombardi
;;Federica Lombardi
Chapter 3
[ 107 ]
8. In the same transformaon, drag a Text le Input step to the canvas and read the
downloaded le.
The ID and country have values only in the rst of the two
lines for each country. In order to repeat the values in the
second line use the ag Repeat in the Fields tab. Set it to Y.
9. Expand the Lookup category of steps.
10. Drag a Stream lookup step to the canvas.
11. Create a hop from the Text le input you just created, to the Stream lookup step.
12. Create another hop from the Filter rows step to the Stream lookup step.
13. Edit the Stream lookup step by double-clicking it.
14. In the Lookup step drop-down list, select Filter ocial languages, the step that brings
the list of languages.
15. Fill the grids in the conguraon window as follows:
Note that Country Name is a eld coming from the text le stream, while the country
eld comes from the countries stream.
Basic Data Manipulaon
[ 108 ]
16. Click OK.
17. The hop that goes from the Filter rows step to the Stream lookup step changes its look
and feel, to show that this is the stream where the Stream lookup is going to look:
18. Aer the Stream lookup, add a Filter rows step.
19. In the Filter rows step, type the condion language-IS NOT NULL.
20. By using a Select values step, rename the elds Duet, Country Name and
language to Name, Country, and Language.
21. Drag a Text le output step to the canvas and create the le
people_and_languages.txt with the selected elds.
22. Save the transformaon.
23. Run the transformaon and check the nal le, which should look like this:
Mikhail Davydova|Russia|
Anastasia Davydova|Russia|
Carmen Rodriguez|Spain|Spanish
Francisco Delgado|Spain|Spanish
Natsuki Harada|Japan|Japanese
Emiko Suzuki|Japan|Japanese
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Lin Jiang|China|Chinese
Wei Chiu|China|Chinese
Chelsea Thompson|United States|English
Cassandra Sullivan|United States|English
Mackenzie Martin|Canada|French
Nathan Gauthier|Canada|French
Giovanni Lombardi|Italy|Italian
Federica Lombardi|Italy|Italian
What just happened?
First of all, you read a le with informaon about countries and the languages spoken in
those countries.
Then you read a list of people along with the country they come from. For every row in this
list, you told Kele to look for the country (Country Name eld) in the countries stream
(country eld), and to give you back a language and the percentage of people that speaks
that language (language and percentage elds). Let's explain it with a sample row: The
row for Francisco Delgado from Spain. When this row gets to the Stream lookup step,
Kele looks in the list of countries for a row with the country Spain. It nds it. Then, it
returns the value of the columns language and percentage: Spanish and 74.4.
Now take another sample row—the row with the country Russia. When the row gets to the
Stream lookup step, Kele looks for it in the list of countries, but it doesn't nd it. So what
you get as language is a null string.
Whether the country is found or not, two new elds are added to your stream—language
and percentage.
Aer the Stream lookup step, you discarded the rows where language is null, that is, those
whose country wasn't found in the list of countries.
With the successful rows you generated an output le.
The Stream lookup step
The Stream lookup step allows you to look up data in a secondary stream.
You tell Kele which of the incoming streams is the stream used to look up, by selecng the
right choice in the Lookup step list.
The upper grid in the conguraon window allows you to specify the names of the elds that
are used to look up.
Basic Data Manipulaon
[ 110 ]
In the le column, Field, you indicate the eld of your main stream. You can ll this
column by using the Get Fields buon, and deleng all the elds you don't want to use
for the search.
In the right column, Lookup Field, you indicate the eld of the secondary stream.
When a row of data comes to the step, a lookup is made to see if there is a row in the
secondary stream for which, every pair (Field, LookupField) in the grid has the value of
Field equal to the value of LookupField. If there is one, the look up will be successful.
In the lower grid, you specify the names of the secondary stream elds that you want back
as a result of the look up. You can ll this column by using the Get lookup elds buon, and
deleng all the elds you don't want to retrieve.
Aer the lookup, new elds are added to your dataset—one for every row of this grid.
For the rows for which the look up is successful, the values for the new elds will be taken
from the lookup stream.
For the others, the elds will remain null, unless you set a default value.
Chapter 3
[ 111 ]
When you use a Stream lookup, all lookup data is loaded into memory. Then the stream
lookup is made using a hash table algorithm. Even if you don't know how this algorithm
works, it is important that you know the implicaons of using this step:
First, if the data where you look is huge, you take the risk of running out
of memory.
Second, only one row is returned per key. If the key you are looking for is present
more than once in the lookup stream, only one will be returned—for example, in the
tutorial where there are more than one ocial languages spoken in a country, you
get just one. Somemes you don't care, but on some occasions this is not acceptable
and you have to try some other methods. You'll learn other ways to do this later in
the book.
Have a go hero – counting words more precisely
The tutorial where you counted the words in a le worked prey well, but you may have
noced that it has some details you can x or enhance.
You discarded a very small list of words, but there are much more that are quite usual
in English—preposions, pronouns, auxiliary verbs, and many more. So here is the challenge:
Get a list of commonly used words and save it in a le. Instead of excluding words from a
small list as you did with a Filter rows step, exclude the words that are in your common
words le.
Use a Stream lookup step.
Test the transformaon with the same le, and also with other les, and verify
that you get beer results with all these changes.
Basic Data Manipulaon
[ 112 ]
This chapter covered the simplest and most common ways of transforming data. Specically,
it covered how to:
Use dierent transformaon steps to calculate new elds
Use the Calculator and the Formula steps
Filter and sort data
Calculate stascs on groups of rows
Look up data
Aer learning basic manipulaon of data, you may now create more complex
transformaons, where the streams begin to split and merge. That is the core
subject of the next chapter.
Controlling the Flow of Data
In the previous chapter, you learned the basics of transforming data. Basically
you read data from some le, did some transformaon to the data, and sent
the data back to a dierent output. This is the simplest scenario. Think of
a dierent situaon. Suppose you collect results from a survey. You receive
several les with the data and those les have dierent formats. You have to
merge those les somehow and generate a unied view of the informaon.
You also want to put aside the rows of data whose content is irrelevant. Finally,
based on the rows that interest you, you want to create another le with some
stascs. This kind of requirement is very common. In this chapter you will
learn how to implement it with PDI.
Splitting streams
Unl now, you have been working with simple, straight ows of data. When you deal with
real problems, those simple ows are not enough. Many mes, the rows of your dataset
have to take dierent paths. This situaon is handled very easily, and you will learn how to
do it in this secon.
Controlling the Flow of Data
[ 114 ]
Time for action – browsing new PDI features by copying
a dataset
Before starng, let's introduce the Pentaho BI Plaorm Tracking site. At the tracking site you
can see the current Pentaho roadmap and browse their issue tracking system. The PDI page
for that site is
In this exercise, you will export the list of proposed new features for PDI from the site, and
generate detailed and summarized les from that informaon.
1. Access the main Pentaho tracking site page:
2. In the main menu, click on FIND ISSUES.
3. On the le side, select the following lters:
Project: Pentaho Data Integraon {Kele}
Issue Type: New Feature
Status: Open
4. At the boom of the lter list, click View >>. A list of found issues will appear.
Chapter 4
[ 115 ]
5. Above the list, select Current eld to export the list to an Excel le.
6. Save the le to the folder of your choice.
The Excel le exported from the JIRA site is a Microso Excel 97-
2003 Worksheet. PDI doesn't recognize this version of worksheets.
So, before proceeding, open the le with Excel or Calc and convert
it to Excel 97/2000/XP.
7. Create a transformaon.
8. Read the le by using an Excel Input step. Aer selecng the le, click on the Sheets
tab, and ll it as shown in the next screenshot so that it skips the header rows and
the rst column:
9. Click the Fields tab and ll the grid by clicking the Get elds from header
row... buon.
Controlling the Flow of Data
[ 116 ]
10. Click the Preview rows just to be sure that you are reading the le properly. You
should see all the contents of the Excel le except the three heading lines.
11. Click OK.
12. Add a Filter rows step to drop the rows where the Summary eld is null.
13. Aer the Filter rows step, add a Value Mapper step and ll it like here:
14. Aer the Value Mapper step, add a Sort rows step and order the rows by
priority_order (asc.), Summary (asc.).
Chapter 4
[ 117 ]
15. Aer that add an Excel Output step, and congure it to send the priority_order
and Summary elds to an Excel le named new_features.xls.
16. Drag a Group by step to the canvas.
17. Create a new hop from the Sort rows step to the Group by step.
18. A warning window appears asking you to decide whether you wish to Copy or
19. Click Copy to send the rows toward both output steps.
20. The hops leaving the Sort rows step change to show you the decision you made. So
far you have this:
21. Congure the Group by steps like shown:
22. Add a new Excel Output step to the canvas and create a hop from the Group by step
to this new step.
Controlling the Flow of Data
[ 118 ]
23. Congure the Excel Output step to send the Priority and Quantity elds to an
Excel le named new_features_summarized.xls.
24. Save the transformaon and run it.
25. Verify that both les, new_features.xls and new_features_summarized.xls,
have been created.
26. The rst le should look like this:
27. And the second le should look like this:
Chapter 4
[ 119 ]
What just happened?
Aer exporng an Excel le with the PDI new features from the JIRA site, you read the le and
created two Excel les—one with a list of the issues and the other with a summary of the list.
The rst steps of the transformaon are well known—read a le, lter null rows, map a eld,
and sort.
Note that the mapping creates a new eld to give an order to the Priority
eld so that the more severe issues are rst in the list, while the minor priories
remain at the end of the list.
You linked the Sort rows step to two dierent steps. This caused PDI to ask you what to
do with the rows leaving the step. By answering Copy, you told PDI to create a copy of
the dataset. Aer that, two idencal copies le the Sort rows step, each to a dierent
desnaon step.
From the moment you copied the dataset, those copies became independent, each following
its way. The rst copy was sent to a detailed Excel le. The other copy was used to create a
summary of the elds, which then was sent to another Excel le.
Copying rows
At any place in a transformaon, you may decide to split the main stream into two or more
streams. When you do so, you have to decide what to do with the data that leaves the last
step—copy or distribute.
To copy means that the whole dataset is copied to each of the desnaon steps. Once the
rows are sent to those steps, each follows its own way.
When you copy, the hops that leave the step from which you are copying change visually to
indicate the copy acon.
Controlling the Flow of Data
[ 120 ]
In the tutorial, you created two copies of the main dataset. You could have created more
than two, like in this example:
When you split the stream into two or more streams, you can do whatever you want with
each one as if they had never been the same. The transformaons you apply to any of those
output streams will not modify the data in the others.
You shouldn't assume a parcular order in the execuon of the output
streams of a step. All the output streams receive the rows in synch and
you don't have control over the order in which they are executed.
Have a go hero – recalculating statistics
Do you remember the exercise from Chapter 3 where you calculated some stascs? You
created two transformaons. One was to generate a le with students that failed. The other
was to create a le with some stascs such as average grade, number of students who
failed, and so.
Now you can do all that work in a single transformaon, reading the le once.
Distributing rows
As said, when you split a stream, you can copy or distribute the rows. You already saw that
copy is about creang copies of the whole dataset and sending each of them to each output
stream. To distribute means the rows of the dataset are distributed among the desnaon
steps. Let's see how it works through a modied exercise.
Chapter 4
[ 121 ]
Time for action – assigning tasks by distributing
Let's suppose you want to distribute the issues among three programmers so that each of
them implements a subset of the new features.
1. Select Transformaon | Copy transformaon to clipboard in the main menu.
Close the transformaon and select Transformaon | Paste transformaon from
clipboard. A new transformaon is created idencal to the one you copied. Change
the descripon and save the transformaon under a dierent name.
2. Now delete all the steps aer the Sort rows step.
3. Change the lter step to keep only the unassigned issues: Assignee eld equal to
the string Unassigned. The condion looks like the next screenshot:
4. From the Transform category of steps, drag an Add sequence step to the canvas and
create a hop from the Sort rows step to this new step.
5. Double-click the Add sequence step and replace the content of the Name of value
textbox with nr.
6. Drag three Excel Output steps to the canvas.
7. Link the Add sequence step to one of these steps.
Controlling the Flow of Data
[ 122 ]
Congure the Excel Output step to send the elds nr, Priority, and Summary to an Excel
le named f_costa.xls (the name of one of the programmers). The Fields tab should look
like this:
8. Create a hop from the Add sequence step to the second Excel Output step. When
asked to decide between Copy and Distribute, select Distribute.
9. Congure the step like before, but name the le as b_bouchard.xls
(the second programmer).
10. Create a hop from the Add sequence step to the last Excel Output step.
11. Congure this last step like before, but name the le as a_mercier.xls
(the last programmer).
12. The transformaon should look like the following:
Chapter 4
[ 123 ]
13. Run the transformaon and look at the execuon tab window to see
what happened:
14. To see which rows belong to which of the created les, open any of them. It should
look like this:
What just happened?
You distributed the issues among three programmers.
In the execuon window, you could see that 84 rows leave the Add sequence step, and 28
arrive to each of the Excel Output steps, that is, a third of the number of rows to each of
them. You veried that when you explored the Excel les.
In the transformaon, you added an Add sequence step that did nothing more than adding
a sequenal number to the rows. This sequence helps you recognize that one out of every
three rows were distributed to every le.
Controlling the Flow of Data
[ 124 ]
Here you saw a praccal example for the distribung opon. When you distribute, the
desnaon steps receive the rows in turn. For example, if you have three target steps, the
rst row goes to the rst target step, the second row goes to the second step, the third row
goes to the third step, the fourth row now goes to the rst step, and so on.
As you could see, when distribung, the hop leaving the step from which you distribute is
plain; it doesn't change its look and feel.
Despite this example showing clearly how the Distribute… method works, this is not how
you will regularly use this opon. The Distribute… opon is mainly used for performance
reasons. Throughout this book you will always use the Copy… opon. To avoid being asked
for the acon to take every me you create more that one hop leaving a step, you can set
the Copy… opon as default; you do this by opening the PDI opons window (Edit|Opons
from the main menu) and unchecking the opon Show "copy or distribute" dialog?.
Remember that to see the change applied, you will have to restart Spoon.
Once you have changed this opon, the default method is copying rows. If you want to
distribute rows, you can change the acon by right-clicking the step from which you want
to copy or distribute, selecng Data Movement... in the contextual menu that appears, and
then selecng the desired opon.
Chapter 4
[ 125 ]
Pop quiz – data movement (copying and distributing)
Look at the following transformaons:
If you do a preview on the Steps named Preview, which of the following is true:
a. The number of rows you see in (a) is greater or equal than the number of rows you
see in (b)
b. The number of rows you see in (b) is greater or equal than the number of rows you
see in (a)
c. The dataset you see in (a) is exactly the same as you see in (b) no maer what data
you have in the Excel le.
You can create a transformaon and test each opon to check the results for yourself. To
be sure you understand correctly where and when the rows take one or other way, you can
preview every step in the transformaon, not just the last one.
Splitting the stream based on conditions
In the previous secon you learned to split the main stream of data into two or more
streams. The whole dataset was copied or distributed among the desnaon steps. Now
you will learn how to put condions so that the rows take one way or another depending
on the condions.
Controlling the Flow of Data
[ 126 ]
Time for action – assigning tasks by ltering priorities with the
Filter rows step
Following with the JIRA subject, let's do a more realisc distribuon of tasks among
programmers. Let's assign the serious task to our most experienced programmer,
and the remaining tasks to others.
1. Create a new transformaon.
2. Read the JIRA le and lter the unassigned tasks, just as you did in the
previous tutorial.
3. Add a Filter rows step and two Excel Output steps to the canvas, and link them to
the other steps as follows:
4. Congure one of the Excel Output steps to send the elds, Priority and Summary,
to an Excel le named b_bouchard.xls (the name of the senior programmer).
5. Congure the other Excel Output step to send the elds Priority and Summary to
an Excel le named new_features_to_develop.xls.
6. Double-click the Filter row step to edit it.
7. Enter the condion Priority = Critical OR Priority = Severe.
8. From the rst drop-down list, Send 'true' data to step, select the step that creates
the b_bouchard.xls Excel le.
9. From the other drop-down list, Send 'false' data to step, select the step that creates
the Excel new_features_to_develop.xls Excel le.
10. Click OK.
Chapter 4
[ 127 ]
11. The hops leaving the Filter rows step change to show which way a row will take,
depending on the result of the condion.
12. Save the transformaon.
13. Run the transformaon, and verify that the two Excel les were created.
14. The les should look like this:
Controlling the Flow of Data
[ 128 ]
What just happened?
You sent the list of PDI new features to two Excel les—one le with the crical issues and
the other le with the rest of the issues.
In the Filter row step, you put a condion to evaluate if the priority of a task was severe
or crical. For every row coming to the lter, the condion was evaluated. The rows that
had a severe or crical priority were sent toward the Excel Output step that creates the
b_bouchard.xls le. The rows with another priority were sent towards the other Excel
Output step, the one that creates the new_features_to_develop.xls le.
PDI steps for splitting the stream based on conditions
When you have to make a decision, and upon that decision split the stream in two, you can
use the Filter row step as you did in this last exercise. In this case, the Filter rows step acts as a
decision maker. It has a condion and two possible desnaons. For every row coming to the
lter, the step evaluates the condion. Then if the result of the condion is true, it decides
to send the row toward the step selected in the rst drop-down list of the conguraon
window—Send 'true' data to step.
If the result of the condion is false, it sends the row toward the step selected in the second
drop-down list of the conguraon window: Send 'false' data to step.
Somemes you have to make nested decisions; consider the next gure for example:
In the transformaon shown in the preceding diagram, the condions are as simple as tesng
if a eld is equal to a value. In situaons like this you have a simpler way for accomplishing
the same..
Chapter 4
[ 129 ]
Time for action – assigning tasks by ltering priorities with the
Switch/ Case step
Let's use a Switch/Case step to replace the nested Filter Rows steps shown in the
preceding diagram
1. Create a transformaon like the following:
2. You will nd the Switch/Case step in the Flow category of steps.
To save me, you can take the last transformaon you created
as the starng point.
Controlling the Flow of Data
[ 130 ]
3. Note that the hops arriving to the Excel Output steps look strange. They are doed
orange lines. This look and feel shows you that the target steps are unreachable. In
this case, it means that you sll have to congure the Switch/Case step. Double-click
it and ll it like here:
4. Save the transformaon and run it
5. Open the Excel les generated to see that the transformaon distributed the task among
the les based on the given condions.
What just happened?
In this tutorial you learned to use the Switch/Case step. This step routes rows of data to one
or more target steps based on the value encountered in a given eld.
In the Switch/Case step conguraon window, you told Kele where to send the row
depending on a condion. The condion to evaluate was the equality of the eld set in Field
name to switch and the value indicated in the grid. In this case, the eld name to switch
is Priority, and the values against which it will be compared are the dierent values for
priories: Severe, Crical, and so on. Depending on the values of the Priority eld, the rows
will be sent to any of the target steps. For example, the rows where Priority=Medium, will be
sent toward the target step New Features for Federica Costa.
Note that it is possible to specify the same target step more than once.
The Default target step represents the step where the rows that don't match any of the case
values are sent. In this example, the rows with a priority not present in the list will be sent to
the step New Features without priority.
Chapter 4
[ 131 ]
Have a go hero – listing languages and countries
Open the transformaon you created in the Finding out which language people speak
tutorial in Chapter 3. If you run the transformaon and check the content of the output le,
you'll noce that there are missing languages. Modify the transformaon so that it generates
two les—one with the rows where there is a language, that is, the rows for which the
lookup didn't fail, and another le with the list of countries not found in the countries.
xml le.
Pop quiz – splitting a stream
Connuing with the contestant exercise, suppose that the number of interpreters you will
hire depends on the number of people that speak each language:
Number of people that speaks the language Number of interpreters
Less than 3 1
Between 3 and 6 2
More that 6 3
You want to create a le with the languages with a single interpreter, another le with the
languages with two interpreters, and a nal le with the languages with three interpreters.
Which of the following would solve your situaon when it comes to spling the languages
into three output streams:
a. A Number range step followed by a Switch/Case step.
b. A Switch/Case step.
c. Both
In order to gure out the answer, create a transformaon and count the number
of people that speak each language. You'll have to use a Sort rows step followed
by a Group by step. Aer that, try to develop each alternave soluon and see
what happens.
Merging streams
You've just seen how the rows of a dataset can take dierent paths. Here you will learn the
opposite—how data coming from dierent places is merged into a single stream.
Controlling the Flow of Data
[ 132 ]
Time for action – gathering progress and merging all together
Suppose that you delivered the Excel les you generated in the Assigning tasks by ltering
priories tutorial earlier in the chapter. You gave the b_bouchard.xls to Benjamin
Bouchard, the senior programmer. You also gave the other Excel le to a project leader who
is going to assign the tasks to dierent programmers. Now they are giving you back the
worksheets, with a new column indicang the progress of the development. In the case of
the shared le, there is also a column with the name of the programmer who is working on
every issue. Your task is now to unify those sheets.
Here is what the Excel les look like:
1. Create a new transformaon.
2. Drag an Excel Input step to the canvas and read one of the les.
3. Add a Filter row step to keep only the rows where the progress is not null, that is,
the rows belonging to tasks whose development has been started.
4. Aer the lter, add a Sort rows step, and congure it to order the elds by
Progress, in descending order.
Chapter 4
[ 133 ]
5. Add another Excel Input step, read the other le, and lter and sort the rows just
like you did before. Your transformaon should look like this:
6. From the Transform category of steps, select the Add Constants step and drag it
onto the canvas.
7. Link the step to the stream that reads the B. Bouchard's le; edit the step and add a
new eld named Programmer, with type string and value Benjamin Bouchard.
8. Aer this step, add a Select values step and reorder the elds so that they remain
in a specic order Priority, Summary, Programmer, Progress—to resemble the
other stream.
9. Now, from the Transform category add an Add sequence step, name the new eld
ID, and link the step with the Select values step.
10. Create a hop from the Sort rows step of the other stream to the Add sequence step.
Your transformaon should look like the one shown next:
Controlling the Flow of Data
[ 134 ]
11. Select the Add sequence step and do a preview. You will see this:
What just happened?
You read two similar Excel les and merged them into one single dataset.
First of all, you read, ltered, and sorted the les as usual. Then you altered the stream
belonging to B. Bouchard, so it looked similar to the other. You added the eld Programmer,
and reordered the elds.
Aer that, you used an Add sequence step to create a single dataset containing the rows of
both streams, with the rows numbered.
PDI options for merging streams
You can create a union of two or more streams anywhere in your transformaon. To create a
union of two or more data streams, you can use any step. The step unies the data, takes the
incoming streams in any order, and then it completes its task in the same way as if the data
came from a single stream.
In the example, you used an Add sequence step as the step to join two streams. The step
gathered the rows from the two streams, and then proceeded to numerate the rows with
the sequence name ID.
Chapter 4
[ 135 ]
This is only one example of how you can mix streams together. As said, any step can be used
to unify two streams. Whichever the step, the most important thing you have to have in
mind is that you cannot mix rows that have a dierent layout. The rows have to have the
same lengths, the same data types, and the same elds in the same order.
Fortunately, there is a trap detector that provides warnings at design me if a step is
receiving mixed layouts.
You can try this out. Delete the Select values step. Create a hop from the Add constants step
to the Add sequence step. A warning message appears as shown next:
In this case, the third eld of the rst stream, Programmer (String), does not have the
same name or the same type as the third eld of the second stream, Progress (Number).
Note that PDI warns you but it doesn't prevent you from mixing row layouts
when creang the transformaon.
If you want Kele to prevent you from running transformaons with mixed row
layouts, you can check the opon Enable safe mode in the window that shows
up when you dispatch the transformaon. Have in mind that doing this will
cause a performance drop.
Controlling the Flow of Data
[ 136 ]
When you use an arbitrary step to unify, the rows remain in the same order as they
were in their original stream, but the streams are joined in any order. Take a look at the
example's preview. The rows of the Bouchard's stream as well as the rows of the other
stream remained sorted within its original group. However, whether the Bouchard's stream
appeared before or aer the rows of the other stream was just a maer of chance. You
didn't decide the order of the streams; PDI decided it for you. If you care about the order in
which the union is made, there are some steps that can help you. Here are the opons
you have:
If you want to ... You can do this ...
Append two or more streams, and
don't care about the order
Use any step. The selected step will take all the incoming
streams in any order, and then will proceed with its specic
Append two streams in a given order Use the Append streams step from the Flow category. It
helps to decide which stream goes rst.
Merge two streams ordered by one or
more elds
Use a Sorted Merge step from the Joins category. This
step allows you to decide on which eld(s) to order the
incoming rows before sending them to the desnaon
step(s). The input streams must be sorted on that eld(s).
Merge two streams keeping the newest
when there are duplicates
Use a Merge Rows (di) step from the Joins category.
You tell PDI the key elds, that is, the elds that say that
a row is the same in both streams. You also give PDI the
elds to compare when the row is found in both streams.
PDI tries to match rows of both streams, based on the key
elds. Then it creates a eld that will act as a ag, and lls
it as follows:
If a row was only found in the rst stream, the
ag is set to deleted.
If a row was only found in the second stream, the
ag is set to new.
If the row was found in both streams, and the
elds to compare are the same, the ag is set to
If the row was found in both streams, and at least
one of the elds to compare is dierent, the ag
is set to changed.
Let's try one of these opons.
Chapter 4
[ 137 ]
Time for action – giving priority to Bouchard by using
Append Stream
Suppose you want the Bouchard's row before the other rows. You can modify the
transformaon as follows:
1. From the Flow category of steps, drag an Append Streams step to the canvas.
Rearrange the steps and hops so the transformaon looks like this:
2. Edit the Append streams step and select as the Head hop the one belonging to the
Bouchard's rows, and as the Tail hop the other. Doing this, you indicate toPDI how it
has to order the streams.
3. Do a preview on the Add sequence step. You should see this:
Controlling the Flow of Data
[ 138 ]
What just happened?
You changed the transformaon to give priority to Bouchard's issues.
You made it by using the Append Streams step. By telling that the head hop was the one coming
from the Bouchard's le, you got the expected order—rst the rows with the tasks assigned
to Bouchard, sorted by progress descending, and then the rows with the tasks assigned to
other programmers, also sorted by progress descending.
Whether you use arbitrary steps or some of the special steps menoned
here to merge streams, don't forget to verify the layouts of the streams
you are merging. Pay aenon to the warnings of the trap detector and
avoid mixing row layouts.
Have a go hero – sorting and merging all tasks
Modify the previous exercise so that the nal output is sorted by priority. Try two possible
Sort the input streams on their own and then use a Sorted Merge step.
Merge the stream with a Dummy step and then sort.
Which one do you think would give the best performance?
Refer to the Sort rows step issues in Chapter 3.
In which circumstances would you use the other opon?
Have a go hero – trying to nd missing countries
As you saw in the countries exercises, there are missing countries in the countries.xml
le. In fact, the countries are there, but with dierent names. For example, Russia in the
contestant le is Russian Federation in the XML le. Modify the transformaon that
looks for the language. Split the stream in two—one for the rows where a language was
found and the other for the rows where no language was found. For this last stream, use a
Value Mapper step to rename the countries you idened as wrong, that is, rename Russia
as Russian Federation. Then look again for a language now with the new name. Finally,
merge the two streams and create the output le with the result.
Chapter 4
[ 139 ]
In this chapter, you learned dierent opons that PDI oers to combine or split ows of data.
The chapter covered the following:
Copying and distribung rows
Spling streams based on condions
Merging independent streams in dierent ways
With the concepts you learned in the inial chapters, the range of tasks you are able to
perform is already broad. In the next chapter, you will learn how to insert JavaScript code in
your transformaons not only as an alternave to perform some of those tasks, but also as
a way to accomplish other tasks that are complicated or even unthinkable to carry out with
regular PDI steps.
Transforming Your Data
with JavaScript Code and the
JavaScript Step
Whichever transformaon you need to do on your data, you have a big chance
of nding that PDI steps are able to do the job. Despite that, it may happen that
there are not proper steps that serve your requirements, or that an apparently
minor transformaon consumes a lot of steps linked in a very confusing
arrangement dicult to test or understand. Pung colorful icons here and
there is funny and praccal, but there are some situaons like the ones
described above where you inevitably will have to code. This chapter explains
how to do it with JavaScript and the special JavaScript step.
In this chapter you will learn how to:
Insert and test JavaScript code in your transformaons
Disnguish situaons where coding is the best opon, from those where there are
beer alternaves
Doing simple tasks with the JavaScript step
One of the tradional steps inside PDI is the JavaScript step that allows you to code inside
PDI. In this secon you will learn how to use it for doing simple tasks.
Transforming Your Data with JavaScript Code and the JavaScript Step
[ 142 ]
Time for action – calculating scores with JavaScript
The Internaonal Musical Contest menoned in Chapter 4 has already taken place. Each duet
performed twice. The rst me technical skills were evaluated, while in the second, the focus
was on arsc performance.
Each performance was assessed by a panel of ve judges who awarded a mark out of a
possible 10.
The following is the detailed list of scores:
Note that the elds don't t in the screen, so the lines are wrapped and doed lines are
added for you to disnguish each line.
Now you have to calculate, for each evaluated skill, the overall score as well as an
average score.
1. Download the sample le from the Packt website.
2. Create a transformaon and drag a Fixed le input step to the canvas to read
the le.
Chapter 5
[ 143 ]
3. Fill the conguraon window as follows:
4. Press the Get Fields buon. A window appears to help you dene the columns.
5. Click between the elds to add markers that dene the limits. The window will look
like this:
6. Click on Next >. A new window appears for you to congure the elds.
7. Click on the rst eld at the le of the window and change the name to Performance.
Verify that the type is set to String.
Transforming Your Data with JavaScript Code and the JavaScript Step
[ 144 ]
8. To the right, you will see a preview of the data for the eld.
9. Select each eld to the le of the window, change the names, and adjust the types. Set
ID, Country, Duet, and Skill elds as String, and elds from Judge 1 to Judge
5 as Integer.
10. Go back and forth between these two windows as many mes as you need unl you are
done with the denions of the elds.
11. Click on Finish.
12. The grid at the boom is now lled.
13. Set the column Trim type to both for every eld.
14. The window should look like the following:
Chapter 5
[ 145 ]
15. Click on Preview the transformaon. You should see this:
16. From the Scripng category of steps, select a Modied JavaScript Value step and drag it
to the canvas.
17. Link the step to the Fixed le input step, and double-click it to congure it.
18. Most of the conguraon window is blank, which is the eding area. Type the following
text in it:
var totalScore;
var wAverage;
totalScore = Judge1 + Judge2 + Judge3 + Judge4 + Judge5;
wAverage = 0.35 * Judge1 + 0.35 * Judge2
+ 0.10 * Judge3 + 0.10 * Judge4 + 0.10 * Judge5;
19. Click on the Get variables buon.
Transforming Your Data with JavaScript Code and the JavaScript Step
[ 146 ]
20. The grid under the eding area gets lled with the two variables dened in the code.
The window looks like this:
21. Click on OK.
22. Keep the JavaScript step selected and do a preview.
23. This is how the nal data looks like:
Chapter 5
[ 147 ]
What just happened?
You read the detailed list of scores and added two elds with the overall score and an
average score for each evaluated skill.
In order to r