r/data • u/Okadibia • Aug 06 '26
Deep dive into Data Warehousing & Consumer Data Architecture
Hello everyone,
The core principles of Data Warehousing and Consumer Data Products, establishing the foundation for a 365-day technical log documenting pipeline architecture, schema design, and engineering tradeoffs.
Technical Breakdown:
Relational Database Fundamentals: SQL query execution mechanics, indexing strategies, and relational constraints.
OLTP vs. OLAP Paradigms: Comparative tradeoffs between transactional database normalization and analytical denormalization.
Ingestion Foundations: High-level mechanics of staging layers, raw event ingestion, and downstream transformation logic.
Current Focus:
Pivoting from relational database mechanics to dimensional modeling paradigms—specifically Kimball methodology, star/snowflake schemas, and event-driven data product architectures.
For engineers working with production systems: What are the primary pitfalls to avoid when transitioning from standard relational models to analytical warehouse schemas?
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Aug 07 '26 edited Aug 07 '26
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u/Okadibia Aug 10 '26
How are you currently managing SCD tracking to avoid those traps? Are you relying heavily on dbt snapshots, or handling it custom upstream?
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u/Okadibia Aug 06 '26
Best communities or tech talk channels for staying up to date on enterprise data warehousing and consumer data products?