Extract, transform and load (ETL) has traditionally been one of the most popular data integration methods for business intelligence and data warehousing. But as more organizations move toward near-real-time applications, we wondered -- how real time can ETL really get? Can ETL support the requirements of near-real-time applications -- and what should organizations consider when designing these systems and processes? In this podcast, learn when and how ETL can effectively support near-real-time requirements and find out more about the benefits and drawbacks of using ETL for these kinds of projects, from Mark Whitehorn, an expert consultant with real-world experience and insight.

In this 10-minute podcast, appropriate for BI-savvy IT and business professionals, listeners will:

• Learn the benefits and drawbacks of using extract, transform and load (ETL) as the primary data integration method for near-real time applications
• Hear more about the key issues to think about when considering this approach -- and find out whether it's the appropriate method for your needs
• Get expert insight into the potential costs involved with deploying ETL in near-real-time situation

Interested in this topic, but prefer alternative content formats? Try:

• Webcast with PowerPoint slides: Optimizing ETL for demanding data environments