Topic
Warehouse Automation
Model-driven generation, metadata-driven pipelines, the design-time-versus-runtime question, and where automation actually moves the needle.
13 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.
Read →Technique
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.
Read →
Patterns
2
Pattern
Automating data vault generation
Why data vault is the most automatable warehouse methodology, what metadata-driven generation produces, what stays human, and when generation earns adoption.
Read →Pattern
The business case for data warehouse automation
What the benefits of data warehouse automation actually are, the mechanism behind each, the costs the case must carry, and when it holds or collapses.
Read →
Decisions
2
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.
Read →Decision
How to evaluate data warehouse automation tools
A practitioner's framework for evaluating data warehouse automation tools: six axes that matter, the questions that expose weak fits, and worked examples.
Read →
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.
Read →Glossary
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.
Read →Glossary
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.
Read →Glossary
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.
Read →Glossary
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.
Read →Glossary
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.
Read →