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Pentaho Data Integration Beginner's Guide - Second Edition

You're reading from   Pentaho Data Integration Beginner's Guide - Second Edition Get up and running with the Pentaho Data Integration tool using this hands-on, easy-to-read guide with this book and ebook

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781782165040
Length 502 pages
Edition 2nd Edition
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Author (1):
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María Carina Roldán María Carina Roldán
Author Profile Icon María Carina Roldán
María Carina Roldán
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Table of Contents (21) Chapters Close

Preface 1. Getting Started with Pentaho Data Integration 2. Getting Started with Transformations FREE CHAPTER 3. Manipulating Real-world Data 4. Filtering, Searching, and Performing Other Useful Operations with Data 5. Controlling the Flow of Data 6. Transforming Your Data by Coding 7. Transforming the Rowset 8. Working with Databases 9. Performing Advanced Operations with Databases 10. Creating Basic Task Flows 11. Creating Advanced Transformations and Jobs 12. Developing and Implementing a Simple Datamart A. Working with Repositories B. Pan and Kitchen – Launching Transformations and Jobs from the Command Line C. Quick Reference – Steps and Job Entries D. Spoon Shortcuts E. Introducing PDI 5 Features F. Best Practices G. Pop Quiz Answers Index

Chapter 5. Controlling the Flow of Data

In the previous chapters, you learned to transform your data in many ways. Now suppose you collect results from a survey. You receive several files with the data and those files have different formats. You have to merge those files somehow, and generate a unified view of the information. Not only that, you want to remove the rows of data whose content is irrelevant. Finally, based on the rows that interest you, you want to create another file with some statistics. This kind of requirement is very common, but requires more background in PDI.

In this chapter, you will learn how to implement this kind of task with Kettle. In particular, we will cover the following topics:

  • Copying and distributing rows
  • Splitting the stream based on conditions
  • Merging streams

You will also apply these concepts in the treatment of invalid data.

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