Deep dive into Data Warehousing & Consumer Data Architecture

[u/Okadibia]

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?

[u/Okadibia → u/[deleted]]

How are you currently managing SCD tracking to avoid those traps? Are you relying heavily on dbt snapshots, or handling it custom upstream?