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Learning Pentaho Data Integration 8 CE

You're reading from   Learning Pentaho Data Integration 8 CE An end-to-end guide to exploring, transforming, and integrating your data across multiple sources

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788292436
Length 500 pages
Edition 3rd Edition
Languages
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Author (1):
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María Carina Roldán María Carina Roldán
Author Profile Icon María Carina Roldán
María Carina Roldán
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Pentaho Data Integration 2. Getting Started with Transformations FREE CHAPTER 3. Creating Basic Task Flows 4. Reading and Writing Files 5. Manipulating PDI Data and Metadata 6. Controlling the Flow of Data 7. Cleansing, Validating, and Fixing Data 8. Manipulating Data by Coding 9. Transforming the Dataset 10. Performing Basic Operations with Databases 11. Loading Data Marts with PDI 12. Creating Portable and Reusable Transformations 13. Implementing Metadata Injection 14. Creating Advanced Jobs 15. Launching Transformations and Jobs from the Command Line 16. Best Practices for Designing and Deploying a PDI Project

Summary


In this chapter, you learned how to insert code into your PDI transformations.

In the first place, you learned how to insert Javascript code. You used this option both to modify and to add new fields to your dataset. You also used Javascript code for filtering data, and for dealing with unstructured input data.

After that, you learned to use the UDJC step for inserting Java code. You used this option to insert and modify fields, and also to control the flow of data.

Besides, you received tips and directions to parse XML and JSON structures by coding. You also saw a list of the pros and cons of coding inside your Transformations, as well as alternative ways to do things, such as avoiding writing code when possible.

If you feel confident with all you have learned until now, you are certainly ready to move on to the next chapter. There, you will learn how to solve more advanced problems such as aggregating data or normalizing data from pivoted tables in a simple fashion.

 

 

 

 

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