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Learning Pentaho Data Integration 8 CE - Third Edition

You're reading from  Learning Pentaho Data Integration 8 CE - Third Edition

Product type Book
Published in Dec 2017
Publisher Packt
ISBN-13 9781788292436
Pages 500 pages
Edition 3rd Edition
Languages

Table of Contents (23) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Getting Started with Pentaho Data Integration Getting Started with Transformations Creating Basic Task Flows Reading and Writing Files Manipulating PDI Data and Metadata Controlling the Flow of Data Cleansing, Validating, and Fixing Data Manipulating Data by Coding Transforming the Dataset Performing Basic Operations with Databases Loading Data Marts with PDI Creating Portable and Reusable Transformations Implementing Metadata Injection Creating Advanced Jobs Launching Transformations and Jobs from the Command Line Best Practices for Designing and Deploying a PDI Project

Going forward and backward across rows


Besides the common use cases explained in the previous sections, there are other use cases that work with groups of rows, looking for rows before or after the current one within each group.

Some examples of this are as follows:

  • You have a dataset with monthly sales, group by product line. For each product line, you want to calculate the variation of sales from one month to the next.
  • You have daily sales and want to infer the number of days without sales. (This is the gap in days between a date and the next in your dataset.)
  • You have a dataset with a list of sales amounts and sales commissions. The fields in your dataset are sales_amount_from, sales_amount_to, and commission_%. You detected that there are overlaps in the data:
sales_amount_from, sales_amount_to, commission_%
0, 1000, %5
1001, 5000, %15
4500, 9999, %15

You want to automatically fix these overlaps. In this case, you want to change the second row to the following:

1001, 4499, %15

In all these examples...

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