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

Using the Table_Comparison transform


The Table_Comparison transform compares a dataset generated inside a dataflow to a target table dataset and changes the statuses of data set rows to different types according to the conditions specified in the Table_Comparison transform.

Data Services uses primary key values for the row comparison and marks the passing row accordingly as: an insert row, which does not exist in the target table yet; an update row, the row for which primary key values exist in the target table but whose non-primary key fields (or comparison fields) have different values; and finally, a delete row (when the target dataset has rows with primary key values that do not exist in the source data set generated inside a dataflow). In some way, Table_Comparison does exactly the same thing as Map_Operation: it changes the row type of passing rows from normal to insert, update, or delete. The difference is that it does it in a smart way—after comparing the dataset to the target table...