Making sense of data warehouse layers (and when to actually store vs. compute them)
Original: Materialization of Data Warehouse Layers

Summary
This article explores the practical application of the 4 transformation layers (raw, staging, dimensional, and reporting) in a data warehouse architecture. It covers topics like fact/dimension modeling, materialized views, and pipeline orchestration to ensure structure and maintainability in your data infrastructure.
Who This Is For
Key Takeaways
- Learn the trade-offs between materializing data as tables vs. keeping them as views based on cost and performance
- Understand how to analyze your cloud warehouse spending to make informed decisions about compute vs. storage optimization
- Discover practical strategies for implementing the 4-layer data warehouse architecture (raw, staging, dimensional, reporting)
- Get specific recommendations on which layers to materialize as tables vs. views for optimal performance and cost
Tools & Technologies
Topics Covered
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