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

Getting data from HBase


Sources like HBase are dynamically loaded and can have data structured in very different ways than what typical sources are known for. Unlike flat files or traditional relational databases, where there is a somewhat rigid data model, tables in a NoSQL database can be free form. There are several ways in which to query such a database, from writing Java code, using Hive to translate a SQL-like statement into an executable plan, or using a tool like Kettle to extract the needed data. For this recipe, we will be utilizing the HBase Input step to load data from HBase.

Getting ready

In order to follow this recipe, you will need to perform the Loading data into HBase recipe. We will be using the dataset created in HBase with that recipe, to answer the question posed while designing the data model—which players attended what school at a given year?

How to do it...

Perform the following steps to get the baseball data from HBase:

  1. Create a new transformation.

  2. Place an HBase Input...