Book Image

Oracle Data Integrator 11g Cookbook

Book Image

Oracle Data Integrator 11g Cookbook

Overview of this book

Oracle Data Integrator (ODI) is Oracle's strategic data integration platform for high-speed data transformation and movement between different systems. From high-volume batches, to SOA-enabled data services, to trickle operations, ODI is a cutting-edge platform that offers heterogeneous connectivity, enterprise-level deployment, and strong administrative, diagnostic, and management capabilities."Oracle Data Integrator 11g Cookbook" will take you on a journey past your first steps with ODI to a new level of proficiency, lifting the cover on many of the internals of the product to help you better leverage the most advanced features.The first part of this book will focus on the administrative tasks required for a successful deployment, moving on to showing you how to best leverage Knowledge Modules with explanations of their internals and focus on specific examples. Next we will look into some advanced coding techniques for interfaces, packages, models, and a focus on XML. Finally the book will lift the cover on web services as well as the ODI SDK, along with additional advanced techniques that may be unknown to many users.Throughout "Oracle Data Integrator 11g Cookbook", the authors convey real-world advice and best practices learned from their extensive hands-on experience.
Table of Contents (19 chapters)
Oracle Data Integrator 11g Cookbook
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Using variables to filter data based on a timestamp


One of the most frequent uses of variables is to apply a data filter to an ODI procedure or interface. Once a variable has been defined either globally or within a project, it can be populated using one of several different methods (that is, refreshed, assigned, passed as a parameter, and so on). Once the variable has acquired a value, it can be used as a filter condition within another project component. ODI currently supports four variable data types (alphanumeric, numeric, text, and date), any of which can be used as a data filter. In this recipe, we will discover how to use variables in order to filter data based on a timestamp.

Getting ready

All references to tables in this recipe are taken from the data samples described in the Preface of this book. Be sure to reverse engineer the DEMO_SRC and DEMO_TRG data models before beginning this recipe. Also, be sure to import the IKM – SQL Control Append KM.

How to do it...

  1. Within your project...