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Pentaho Data Integration 4 Cookbook

Pentaho Data Integration 4 Cookbook

4.8 (8)
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Pentaho Data Integration 4 Cookbook

Pentaho Data Integration 4 Cookbook

4.8 (8)

Overview of this book

Pentaho Data Integration (PDI, also called Kettle), one of the data integration tools leaders, is broadly used for all kind of data manipulation such as migrating data between applications or databases, exporting data from databases to flat files, data cleansing, and much more. Do you need quick solutions to the problems you face while using Kettle? Pentaho Data Integration 4 Cookbook explains Kettle features in detail through clear and practical recipes that you can quickly apply to your solutions. The recipes cover a broad range of topics including processing files, working with databases, understanding XML structures, integrating with Pentaho BI Suite, and more. Pentaho Data Integration 4 Cookbook shows you how to take advantage of all the aspects of Kettle through a set of practical recipes organized to find quick solutions to your needs. The initial chapters explain the details about working with databases, files, and XML structures. Then you will see different ways for searching data, executing and reusing jobs and transformations, and manipulating streams. Further, you will learn all the available options for integrating Kettle with other Pentaho tools. Pentaho Data Integration 4 Cookbook has plenty of recipes with easy step-by-step instructions to accomplish specific tasks. There are examples and code that are ready for adaptation to individual needs.
Table of Contents (17 chapters)
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Pentaho Data Integration 4 Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Interspersing new rows between existent rows


In most Kettle datasets, all rows share a common meaning; they represent the same kind of entity, for example:

  • In a dataset with sold items, each row has data about one item

  • In a dataset with the mean temperature for a range of days in five different regions, each row has the mean temperature for a different day in one of those regions

  • In a dataset with a list of people ordered by age range (0-10, 11-20, 20-40, and so on), each row has data about one person

Sometimes, there is a need of interspersing new rows between your current rows. Taking the previous examples, imagine the following situations:

  • In the sold items dataset, every 10 items, you have to insert a row with the running quantity of items and running sold price from the first line until that line.

  • In the temperature's dataset, you have to order the data by region and the last row for each region has to have the average temperature for that region.

  • In the people's dataset, for each age range...

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