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

The XML_Map transform


In the first recipe of this chapter, Working with nested structures, we built the nested structure with the help of the most universal transform in Data Services—Query transform. Query transform has the power to define column mapping, filter data, join datasets together, and merge data in nested segments. In fact, many transforms that you have used before, such as History_Preserving, Table_Comparison, Pivot, and others, can be substituted with the set of Query transforms. Of course, those would be complex ETL solutions requiring more development time, would be harder to maintain and read, and, most importantly, less efficient in terms of performance.

In this recipe, we will take a look at another transform XML_Map, which does exactly the same task as performed in the previous recipe—builds and transforms nested structures.

We will use the same source tables PERSON.PERSON and HUMANRESOURCES.EMPLOYEE to build a dataset of job titles with nested lists of employees.

Getting...