Architecture
Decisions and trade-offs.
Direct comparisons between approaches and frameworks for the structural choices that recur across warehouse projects: ETL versus ELT, dimensional versus vault, lakehouse versus warehouse, and the platform selection that follows from them.
6 entries
Decision
Data warehouse automation vs AI coding agents: where the logic lives
Data warehouse automation vs AI coding agents: a build-vs-buy framework for your data stack, by scale, correctness stakes, and who owns the warehouse logic.
Comparison
Data warehouse vs data lake vs data mart vs lakehouse
Data warehouse vs data lake vs data mart vs lakehouse: four distinct architectural commitments, what each one actually is, how they compare on storage, governance, query engine, and workload, and when each is the right choice in a 2026 stack.
Comparison
ETL vs ELT
ETL vs ELT: what the order of operations actually changes, why cloud columnar warehouses shifted the default from ETL to ELT, the trade-offs that determine which pattern fits a given workload, and a note on where reverse ETL fits.
Comparison
OLTP vs OLAP: which workload goes where
OLTP vs OLAP: what each is optimized for, how HTAP and columnar cloud warehouses blurred the line, and which side a given workload actually belongs on.
Decision
Referential integrity in a data warehouse
Referential integrity in a data warehouse is a decision, not a default. A framework for choosing between database-enforced foreign keys, informational constraints, ELT-layer assertions, and unenforced declarations on Snowflake, BigQuery, Redshift, Databricks, and lakehouse table formats.
Comparison
Star schema vs snowflake schema
Star schema vs snowflake schema: when to denormalize the whole dimensional model, when to keep hierarchies normalized, and what changes on modern columnar warehouses where the textbook trade-offs no longer hold.