Book Image

Pentaho Data Integration Cookbook - Second Edition

Book Image

Pentaho Data Integration Cookbook - Second Edition

Overview of this book

Pentaho Data Integration is the premier open source ETL tool, providing easy, fast, and effective ways to move and transform data. While PDI is relatively easy to pick up, it can take time to learn the best practices so you can design your transformations to process data faster and more efficiently. If you are looking for clear and practical recipes that will advance your skills in Kettle, then this is the book for you. Pentaho Data Integration Cookbook Second Edition guides you through the features of explains the Kettle features in detail and provides easy to follow recipes on file management and databases that can throw a curve ball to even the most experienced developers. Pentaho Data Integration Cookbook Second Edition provides updates to the material covered in the first edition as well as new recipes that show you how to use some of the key features of PDI that have been released since the publication of the first edition. You will learn how to work with various data sources – from relational and NoSQL databases, flat files, XML files, and more. The book will also cover best practices that you can take advantage of immediately within your own solutions, like building reusable code, data quality, and plugins that can add even more functionality. Pentaho Data Integration Cookbook Second Edition will provide you with the recipes that cover the common pitfalls that even seasoned developers can find themselves facing. You will also learn how to use various data sources in Kettle as well as advanced features.
Table of Contents (21 chapters)
Pentaho Data Integration Cookbook Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
References
Index

Processing data into shared transformations via filter criteria and subtransformations


Processing data is one of the key capabilities of any Extract, Transform, and Load (ETL) tool and Kettle is no different. Sometimes though, data must be processed differently (usually due to data quality issues or business rules). If this logic is needed in multiple places, it makes sense to break that code out into its own transformation using a Mapping (sub-transformation).

This recipe will be creating a simple usage of the Mapping (sub-transformation) step.

Getting ready

For this recipe, we will be building off of the Splitting a stream in two or more streams based on a condition recipe, presented earlier in this chapter. It is recommended to follow this recipe and understand how the Switch/Case step works before continuing. Alternatively, the code for this recipe is available on the book's website.

How to do it...

Perform the following steps:

  1. Open the transformation created from the Splitting a stream in...