This session answers architecture questions related to the overall structure and strategy for an enterprise analytics system. How do we design a data warehouse for analytics purposes? What do we integrate into a central warehouse or maintain in separate data marts? How do we integrate long term storage systems that we need to query only occasionally? How do we integrate real-time data streaming? What ETL and integration strategies and tools are we going to use? Where and how do we extend our raw data with calculated measures and analytic functions? How do we create a system that has a strong foundation, but can grow and evolve over time? This presentation will draw from real-world data architecture projects that were designed with analytics in mind. Included will be a discussion of considerations when designing for Autonomous Data Warehouses.
Architecting Analytic Warehouses for Analytics
Presented by Dan Vlamis and Mike Caskey
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