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Making sense of data warehouse layers (and when to actually store vs. compute them)

Original: Materialization of Data Warehouse Layers

Tim Hiebenthal
May 29, 2025
8 min read
Article
Intermediate
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

Analytics Engineers
Data Engineers
Business Analysts

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

Snowflake BigQuery Databricks SQL Cloud Data Warehouses

Topics Covered

data-warehousing etl-pipelines data-modeling data-engineering data-architecture