Book Image

SAP Data Services 4.x Cookbook

Book Image

SAP Data Services 4.x Cookbook

Overview of this book

Want to cost effectively deliver trusted information to all of your crucial business functions? SAP Data Services delivers one enterprise-class solution for data integration, data quality, data profiling, and text data processing. It boosts productivity with a single solution for data quality and data integration. SAP Data Services also enables you to move, improve, govern, and unlock big data. This book will lead you through the SAP Data Services environment to efficiently develop ETL processes. To begin with, you’ll learn to install, configure, and prepare the ETL development environment. You will get familiarized with the concepts of developing ETL processes with SAP Data Services. Starting from smallest unit of work- the data flow, the chapters will lead you to the highest organizational unit—the Data Services job, revealing the advanced techniques of ETL design. You will learn to import XML files by creating and implementing real-time jobs. It will then guide you through the ETL development patterns that enable the most effective performance when extracting, transforming, and loading data. You will also find out how to create validation functions and transforms. Finally, the book will show you the benefits of data quality management with the help of another SAP solution—Information Steward.
Table of Contents (19 chapters)
SAP Data Services 4.x Cookbook
About the Author
About the Reviewers

Using aggregate functions

Aggregate functions are used in dataflow Query transforms to perform aggregation on the grouped dataset.

You should be familiar with these functions as they are the same ones used in the SQL language: avg(), min(), max(), count(), count_distinct(), and sum().

How to do it…

To demonstrate the use of aggregate functions, we will perform a simple analysis of one of our tables. Import the DimGeography table into the DWH datastore and create a new job with a single dataflow inside it using these steps:

  1. Your dataflow should include the DimGeography source table and the DimGeography target template table in a STAGE database to send the output to:

  2. Open the Query transform and create the following output structure:

    The COUNTRYREGIONCODE column contains country code values and will be the column on which we perform the grouping of the dataset. It is mapped from the input dataset to the output. Also, drag and drop it to the GROUP BY tab of the Query transform from the input dataset...