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

Loading data into HBase


HBase is another component in the Hadoop ecosystem. It is a columnar database, which stores datasets based on the columns, instead of the rows that make it up. This allows for higher compression and faster searching, making columnar databases ideal for the kinds of analytical queries that can cause significant performance issues in traditional relational databases.

Note

For this recipe we will be using the Baseball Dataset loaded into Hadoop in the recipe Loading data into Hadoop, (also in this chapter). It is recommended that the recipe Loading data into Hadoop is performed before continuing.

Getting ready

In this recipe, we will be loading the Schools.csv, Master.csv, and SchoolsPlayers.csv files. The data relates (via the SchoolsPlayers.csv file) schools (found in the Schools.csv file) to players (found in the Master.csv file). This data is designed for a relational database, so we will be tweaking the data to take advantage of Hbase's data store capabilities. Before...