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

Processing a large number of files in parallel


If you have worked through the two previous recipes, you should already have in place a framework for detecting the presence of a variable number of files in a designated location. You will also have developed a method for processing each of those files as they appear in a prepared list (that is, a table). But consider a situation where there are hundreds or even thousands of files to be processed. Managing all of those files serially would likely prove to be a bottleneck.

In this recipe, we will enhance the file-processing framework by introducing a way to execute the most critical components in parallel.

Getting ready

We will start from where we left off with the previous recipe and to do that, we will build on what we already have: a table that contains a list of files to be processed.

How to do it...

  1. Create a new package called ProcessFiles.

  2. Create a new variable called FILE_PARAM and set it as Alphanumeric. Drag-and-drop the variable in the...