For decades, organizations have had to integrate data using hand coding or enterprise data integration software. Over the years, many enterprise data integration techniques have evolved, including extract, transform and load (ETL), enterprise application integration (EAI), enterprise information integration (EII) and enterprise data replication (EDR.) These methods are applied differently by organizations and each has advantages and disadvantages.
This series is an excellent way to train and educate people who are new to enterprise data integration. In this series of short audio podcasts (each approximately five minutes), appropriate for those with business or technical backgrounds, listeners will:
- Hear straightforward explanations of various data integration techniques, including ETL, EAI, EII and EDR.
- Learn how each method is commonly applied and find out why organizations might choose to use one method over another.
- Hear more about the advantages and disadvantages of the different approaches
About the expert: Mark Madsen is president of Third Nature, a market research and consulting firm focused on business intelligence, data integration and data management. Mark is an award-winning architect and CTO whose work has been featured in numerous industry publications. He is a principal author of Clickstream Data Warehousing and teaches classes on data warehousing and open source for TDWI. For more information or to contact Mark, visit ThirdNature.net.
Download podcasts here:
Extract,
transform and load (ETL)
Enterprise
application integration (EAI)
Enterprise
information integration (EII)
Enterprise
data replication (EDR)
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More on enterprise data integration
- Find out how companies decide whether to use hand coding or data integration software
- See how Gartner ranked data integration platforms
- Learn more about data integration basics in this podcast with Rick Sherman
- Find out how service-oriented architecture and data integration are related -- and learn more about data-as-a-service
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This was first published in August 2007
Data Management Strategies for the CIO
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