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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Optimizing dataflow execution – push-down techniques


The Extract, Transform, and Load sequence can be modified to Extract, Load, and Transform by delegating the power of processing and transforming data to the database itself where the data is being loaded to.

We know that to apply transformation logic to a specific dataset we have to first extract it from the database, then pass it through transform objects, and finally load it back to the database. Data Services can (and most of the time, should, if possible) delegate some transformation logic to the database itself from which it performs the extract. The simplest example is when you are using multiple source tables in your dataflow joined with a single Query transform. Instead of extracting each table's contents separately onto an ETL box by sending multiple SELECT * FROM <table> requests, Data Services can send the generated single SELECT statement with proper SQL join conditions defined in the Query transform's FROM and WHERE tabs...