There's more than one way to effectively handle data integration for data warehouses. Techniques like change data capture, event stream processing and many others are becoming more popular, replacing or complementing extract, transform and load (ETL) tools. Additionally, technology trends like the rise of service-oriented architectures, are changing the way that organizations approach data integration processes for data warehouses. SearchDataManagement.com recently learned more about trends in data integration for data warehouses from James Kobielus, senior analyst with Forrester Research.
In this 20-minute podcast, listeners will:
- Find out which data integration methods are becoming more popular for data warehousing -- and learn why companies are venturing beyond traditional ETL
- Learn how updating your data integration strategy can support new data warehousing requirements, such as near-real-time business intelligence
- Hear the pros and cons of these different approaches -- and find out how to select the right one for your situation
- Get an overview of relevant technology trends and market announcements, such as new and planned capabilities from Microsoft, Oracle and others
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About the speaker: James Kobielus is a senior analyst at Cambridge, Mass.-based Forrester Research Inc. Kobielus serves information and knowledge management professionals, as a leading analyst and expert on data warehousing, predictive analytics, data mining and complex event processing. In addition to his core coverage areas, Kobielus contributes to Forrester's research in business intelligence, data integration, data quality, and master data management. Kobielus has a long history in IT research and consulting and has worked for both vendors and research firms. He is both well versed and experienced in product and market assessments. Kobielus is a widely published business/technology author and has spoken at many industry events.
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