Book Image

Pentaho 3.2 Data Integration: Beginner's Guide

Book Image

Pentaho 3.2 Data Integration: Beginner's Guide

Overview of this book

Pentaho Data Integration (a.k.a. Kettle) is a full-featured open source ETL (Extract, Transform, and Load) solution. Although PDI is a feature-rich tool, effectively capturing, manipulating, cleansing, transferring, and loading data can get complicated.This book is full of practical examples that will help you to take advantage of Pentaho Data Integration's graphical, drag-and-drop design environment. You will quickly get started with Pentaho Data Integration by following the step-by-step guidance in this book. The useful tips in this book will encourage you to exploit powerful features of Pentaho Data Integration and perform ETL operations with ease.Starting with the installation of the PDI software, this book will teach you all the key PDI concepts. Each chapter introduces new features, allowing you to gradually get involved with the tool. First, you will learn to work with plain files, and to do all kinds of data manipulation. Then, the book gives you a primer on databases and teaches you how to work with databases inside PDI. Not only that, you'll be given an introduction to data warehouse concepts and you will learn to load data in a data warehouse. After that, you will learn to implement simple and complex processes.Once you've learned all the basics, you will build a simple datamart that will serve to reinforce all the concepts learned through the book.
Table of Contents (27 chapters)
Pentaho 3.2 Data Integration Beginner's Guide
Credits
Foreword
The Kettle Project
About the Author
About the Reviewers
Preface
Index

Chapter 7. Validating Data and Handling Errors

So far, you have been working alone in front of your own computer. In the "Time for action" exercises, the step-by-step instructions along with the error-free sample data helped you create and run transformations free of errors. During the "Have a go hero" exercises, you likely encountered numerous errors, but tips and troubleshooting notes were there to help you get rid of them.

This is quite different from real scenarios, mainly for two reasons:

  • Real data has errors—a fact that can't be avoided. If you fail to heed it, the transformations that run with your sample data will probably crash when running with real data.

  • In most cases, who runs your final work is decided by an automated process and is not user defined. Therefore, if a transformation crashes, there will be nobody to fix the problem.

In this chapter you will learn about the options that PDI offers to treat errors and validate data so that your transformations are well prepared to...