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 the name of a file (or part of it) as a field


There are some occasions where you need to include the name of a file as a column in your dataset for further processing. With Kettle, you can do it in a very simple way.

In this example, you have several text files about camping products. Each file belongs to a different category and you know the category from the filename. For example, tents.txt contains tent products. You want to obtain a single dataset with all the products from these files including a field indicating the category of every product.

Getting ready

In order to run this exercise, you need a directory (campingProducts) with text files named kitchen.txt, lights.txt, sleeping_bags.txt, tents.txt, and tools.txt. Each file contains descriptions of the products and their price separated with a |. Consider the following example:

Swedish Firesteel - Army Model|$19.97
Mountain House #10 Can Freeze-Dried Food|$53.50
Coleman 70-Quart Xtreme Cooler (Blue)|$59.99
Kelsyus Floating Cooler...