Topic
Warehouse Automation
Model-driven generation, metadata-driven pipelines, the design-time-versus-runtime question, and where automation actually moves the needle.
10 entries
Techniques
2
Technique
Data warehouse metadata: catalogs, lineage, and repositories
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
6
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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Metadata-driven pipeline
A data pipeline whose behavior is controlled by metadata or configuration — source definitions, mappings, and rules held as data — rather than hand-coded per source, so new sources are onboarded by adding metadata instead of writing pipeline code.
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Model-driven architecture
A design approach in which a formal model is the primary artifact and executable behavior is generated from it rather than hand-written — applied to warehousing by deriving load logic and schema from a data model.
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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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Schema generation
The automatic creation of database schema — table and column definitions, keys, and types — from a higher-level source such as a data model, a source schema, or sample data, rather than writing the DDL by hand.
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