The work of data integration has become increasingly complex in recent years. Business needs for real-time and low latency data, expanded uses of unstructured data, and accelerated interest in big data analytics are but a few of the trends that change the data integration landscape. Extract-transform-load (ETL) processing was sufficient for the once relatively simple task of combining data from multiple transactional databases was to build a data warehouse, operational data store, or master data hub. Today’s data integration challenges go well beyond the capabilities of ETL technologies with needs to integrate enterprise data with external data, Web data, clickstream data, end-user data, big data, cloud data, and more. To meet these new requirements, data integrators need more tools in the integration toolbox. Data virtualization doesn’t replace ETL; it complements ETL and offers new tools to meet new integration needs.

Data virtualization is a core component of next-generation data integration architectures, techniques, and technology. This online training course will introduce you to the concepts, techniques, and capabilities of data virtualization. It will prepare you to expand your data integration capabilities, deliver business-speed information, and make the most of recent advances in data integration technology.

• Data virtualization definitions, concepts, and terminology
• Business case and technical rationale for data virtualization
• Foundational principles of virtualization – abstraction, views, and services
• How to extend the data warehouse with virtualization
• How virtualization is applied for unstructured data, big data, and cloud data challenges
• How to mix and match virtualization with ETL technology to optimize data integration architectures and processes

• BI, MDM, and data warehousing program and project managers
• Data integration architects, designers, and developers
• Data and technology architects