Prerequisite: None

Course Outline

Data integration is becoming increasingly complex as new expectations and technologies change the face of data warehousing and business intelligence. The design of data integration systems was comparatively straightforward when extract, transform, and load (ETL) was the only option. In today's world, the demand for real-time and right-time data increases expectations, while scorecards and dashboards increase visibility. Simultaneously, enterprise information integration (EII), enterprise application integration (EAI), master data management (MDM), and customer data integration (CDI) technologies expand the range of possibilities.

This course teaches techniques and skills to build data integration systems that can meet today’s needs and evolve to meet demands of the future. Starting with the right requirements, using the right technologies, and designing for adaptability are central themes throughout the course.

You Will Learn

• Analysis techniques to capture data integration requirements, including those for source data, data consolidation, data quality, data granularity, data currency, and historical data
• How the alphabet soup of integration technologies—ETL, EII, EAI, MDM, and CDI—fits into overall data integration architecture
• Design techniques for the mainstream of data integration, including source-to-target mapping, source data capture, data transformation and cleansing, and database loading
• Techniques to enrich the data integration design with processes for automated scheduling, execution monitoring, metadata capture, restart and recovery, and more
• Tips to design for the complex issues of data integration, including detecting data changes, identifying data quality defects, managing complex schedule dependencies, meeting real-time data demands, and more

Geared To

• Business intelligence and data warehousing architects
• Data integration process designers and developers
• Business intelligence and data warehousing program and project managers