Book Image

Pentaho Data Integration Cookbook - Second Edition - Second Edition

Book Image

Pentaho Data Integration Cookbook - Second Edition - 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

Using Metadata Injection to re-use transformations


Earlier in this chapter, we covered how to re-use transformations and jobs that utilize the same data structures and allowed for smaller portions of transformations to be broken out and used by several different transformations. Another common pattern is repeating a given process, but having a very different dataset flow. For instance, suppose we wanted to pull data from multiple tables and perform the same kind of logic on the data stream without having to write the transformation over for the different data stream. While some of that functionality could be done with Mappings and parameterization of jobs or transformations, Metadata Injection will allow for a transformation to be reused against different data streams based on the metadata of the stream.

Getting ready

For this recipe, we will be reusing the book_news dataset used in earlier chapters. You can find the files used to create this dataset on the book's website.

How to do it...

This...