Topic
Warehouse Automation
Model-driven generation, metadata-driven pipelines, the design-time-versus-runtime question, and where automation actually moves the needle.
6 entries
Techniques
2
Technique
Data warehouse metadata: catalogs, lineage, and the metadata repository in 2026
How technical, business, and operational metadata get organized in a modern warehouse stack, including the shift from monolithic metadata repositories to federated data catalogs, dbt-driven lineage, and OpenLineage as the cross-tool standard.
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Data warehouse testing: validation, regression, and performance
What to test in a production warehouse pipeline, where each kind of test lives, and how dbt tests, Great Expectations, and contract patterns fit together without producing a green dashboard over wrong data.
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Glossary
3
Glossary
Data catalog
A searchable index over the metadata of the data assets in an analytics platform: tables, columns, dashboards, models, owners, descriptions, and lineage, federated from the upstream tools that produce each piece.
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Data lineage
The recorded graph of how a data value flows from source to destination across the pipeline: which sources fed which models fed which dashboards, at table or column granularity, derived from build artifacts and runtime events rather than maintained by hand.
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Referential integrity
The property that every foreign key value in a child table actually exists in the parent table it references. In a data warehouse, the question of where to enforce this property, in the database engine, in the transformation layer, or not at all, is a long-running design debate that the move to cloud platforms has decisively reshaped.
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