I uploaded the slides from last's week's webcast on operational data integration and open source. They're embedded below for online viewing.

This is an overview of the difference between application integration and data integration, the differences in use and requirements for DI between business intelligence and OLTP, some integration architecture discussion, and why open source is an even better fit in the operational DI arena than it is for BI projects.

If you want to download a PDF of the slides or listen to a replay, you can find this talk under "How to Use the Right Tools for Operational Data Integration" on Talend's webcast page. There's no direct link to the presentation page so you have to click through.

More detailed description of the webcast

Data integration tools were once used solely in support of data
warehousing, but that has been changing over the past few years. The
fastest growing area today for data integration is outside the data
warehouse, whether it's one-time data movement for migrations and
consolidations or real-time data synchronization for master data
management projects.

Data integration tools have proven to be
faster, more flexible and more cost effective for operational data
integration than the common practice of hand-coding or using
application integration technologies. The developer focus of these
technologies also makes them a prime target for open source
commoditization.



During the presentation you will learn about the differences between
analytical and operational data integration, technology patterns and
options, and recommendations for how to begin using tools for
operational data integration.



Key points:

• How to map common project scenarios to integration architectures and
  tools
• The technology and market changes that favor use of tools for
  operational data integration
• The differing requirements for operational vs. analytic data
  integration
• Advantages of open source for data integration tasks
  embed: