Topic
Modern Warehouse Platforms
Snowflake, BigQuery, Redshift, Databricks, and the platform-level trade-offs that shape how a warehouse is built on top of them.
7 entries
Techniques
2
Technique
Data virtualization: federated query in modern warehouse stacks
How data virtualization works as a technique, what it shares with and how it differs from federated query and the logical data warehouse, where it fits in cloud warehouse stacks, and the failure modes that determine when virtualization holds up in production.
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Logical data warehouse: the architectural pattern
The logical data warehouse unifies a physical warehouse with lakehouses, operational stores, and SaaS sources behind a single query layer. How the pattern actually works in 2026, where it fits, and where it quietly breaks.
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Comparisons
2
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.
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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.
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