SAP Data Services 4.x Cookbook

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.

SAP Data Services 4.x Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Introduction to ETL Development

Introduction

Preparing a database environment

Creating a source system database

Defining and creating staging area structures

Creating a target data warehouse

Configuring the Data Services Environment

Introduction

Creating IPS and Data Services repositories

Installing and configuring Information Platform Services

Installing and configuring Data Services

Configuring user access

Starting and stopping services

Administering tasks

Understanding the Designer tool

Data Services Basics – Data Types, Scripting Language, and Functions

Introduction

Creating variables and parameters

Creating a script

Using string functions

Using date functions

Using conversion functions

Using database functions

Using aggregate functions

Using math functions

Using miscellaneous functions

Creating custom functions

Dataflow – Extract, Transform, and Load

Introduction

Creating a source data object

Creating a target data object

Loading data into a flat file

Loading data from a flat file

Loading data from table to table – lookups and joins

Using the Map_Operation transform

Using the Table_Comparison transform

Exploring the Auto correct load option

Splitting the flow of data with the Case transform

Monitoring and analyzing dataflow execution

Workflow – Controlling Execution Order

Introduction

Creating a workflow object

Nesting workflows to control the execution order

Using conditional and while loop objects to control the execution order

Using the bypassing feature

Controlling failures – try-catch objects

Use case example – populating dimension tables

Job – Building the ETL Architecture

Introduction

Projects and jobs – organizing ETL

Using object replication

Migrating ETL code through the central repository

Migrating ETL code with export/import

Debugging job execution

Monitoring job execution

Building an external ETL audit and audit reporting

Using built-in Data Services ETL audit and reporting functionality

Auto Documentation in Data Services

Validating and Cleansing Data

Introduction

Creating validation functions

Using validation functions with the Validation transform

Reporting data validation results

Using regular expression support to validate data

Enabling dataflow audit

Data Quality transforms – cleansing your data

Optimizing ETL Performance

Introduction

Optimizing dataflow execution – push-down techniques

Optimizing dataflow execution – the SQL transform

Optimizing dataflow execution – the Data_Transfer transform

Optimizing dataflow readers – lookup methods

Optimizing dataflow loaders – bulk-loading methods

Optimizing dataflow execution – performance options

Advanced Design Techniques

Introduction

Change Data Capture techniques

Automatic job recovery in Data Services

Simplifying ETL execution with system configurations

Transforming data with the Pivot transform

Developing Real-time Jobs

Introduction

Working with nested structures

The XML_Map transform

The Hierarchy_Flattening transform

Configuring Access Server

Creating real-time jobs

Working with SAP Applications

Introduction

Loading data into SAP ERP

Introduction to Information Steward

Introduction

Exploring Data Insight capabilities

Performing Metadata Management tasks

Working with the Metapedia functionality

Creating a custom cleansing package with Cleansing Package Builder

Index

Customer Reviews

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1 star

Optimizing dataflow loaders – bulk-loading methods

By default, all records inside a dataflow coming to a target table object are sent as separate INSERT commands to a target table at the database level. If millions of records pass the dataflow and transformation happens on the ETL box without push-downs, the performance of sending millions of INSERT commands over the network back to a database for insertion could be extremely slow. That is why it is possible to configure the alternative load methods on the target table object inside a dataflow. These types of loads are called bulk-load loads. Bulk-load methods are different in nature, but all of them have the main principle and achieve the same goal—they avoid the execution of millions of INSERT statements for each migrated record, providing alternative ways of inserting data.

Bulk-load methods executed by Data Services for inserting data into a target table are completely dependent on the type of target database. For example, Oracle Database...

SAP Data Services 4.x Cookbook

SAP Data Services 4.x Cookbook

Overview of this book

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SAP Data Services 4.x Cookbook

Optimizing dataflow loaders – bulk-loading methods