Practical Data Warehouse Design and Architecture Guide

Summary

The guide presents data warehouse design as a business-aligned process for building, modernizing, or scaling analytics infrastructure, rather than merely storing data. It recommends defining analytics use cases and stakeholder needs first, then organizing a three-tier architecture of source, storage, and semantic output layers; cloud designs can decouple compute and storage and use open formats. A Bronze–Silver–Gold medallion flow preserves raw lineage, applies cleansing and deduplication, and produces consumption-ready dimensional models, while retention and archival policies control sprawl. For modeling, it favors star schemas for user-facing BI, uses snowflake normalization when redundancy is material, and stresses explicit fact-table granularity, domain-owned data marts, and workload-specific refresh cadences. Governance and operations include Unity Catalog, access controls, masking, lineage, multi-region deployment, disaster recovery, and CI/CD, followed by phased rollout through high-value domains.

Takeaways

1. 1

   Fact-table granularity should be established early because finer granularity increases storage while maximizing analytical flexibility, and changing granularity later can require costly pipeline rewrites.

2. 2

   Refresh cadence should match each data mart’s workload: the guide contrasts nightly batch updates for finance month-end close with hourly updates for marketing campaign optimization.

3. 3

   Schema changes and new data-mart definitions should pass through version-controlled pull requests, automated tests, and staged environments before production deployment.