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Organizations that start loading data into BigQuery without a design discipline end up with a collection of tables that happen to live in the same GCP project. There is no consistent naming convention. The same entity is modeled differently in different datasets. Some tables have no partitioning because nobody thought about how they would be queried. Queries that should take two seconds scan terabytes instead of gigabytes because the clustering keys are wrong. The data engineers can't tell which tables are in production use and which are ad-hoc explorations from six months ago.
Before designing partitioning and clustering, we analyze the queries that will run against the warehouse: filter columns, join columns, aggregation patterns, and approximate data volumes. The architecture serves the queries — not the other way around.
Medallion layer definitions, dataset structure, naming conventions, and team ownership model. This is the governance layer of the warehouse architecture — documented before any tables are created.
Table schemas designed against the source data and the query patterns. Dimensional models designed with SCD strategies and grain definitions documented. Reviewed with the data engineering team before implementation.
Bring us the constraint. We'll bring the team.
Data warehouse architecture prevents this. It is the upfront investment that makes everything else — pipelines, dashboards, data quality — maintainable at scale.
We apply a layered architecture that separates raw ingested data (Bronze) from cleaned and conformed data (Silver) from business-ready aggregated and modeled data (Gold). This separation creates clear boundaries: pipelines load Bronze, transformation logic produces Silver and Gold, dashboards query Gold. Each layer has a documented purpose, a defined schema ownership, and an access control policy.
Dataset structure design: how to group tables across projects and datasets to reflect data domains, team ownership, and access control requirements. Table design: column selection, data types, nullable vs. required, repeated and nested fields where appropriate. Partitioning strategy: ingestion-time vs. column-based partitioning, partition expiry, and partition filter requirements. Clustering key selection aligned to the query patterns that the visualization layer and analytical workloads will generate.
Dimensional modeling (star schema, snowflake schema, or wide denormalized tables depending on the query patterns). Slowly changing dimension strategies. Surrogate key design. Fact table grain definition — the most important decision in dimensional modeling, and the one most often made incorrectly.
Dataset-level IAM bindings for team and service account access. Column-level security for sensitive fields (PII, financial data) using BigQuery policy tags. Row-level security using authorized views or row access policies. Access model documented so that new datasets and tables follow a consistent policy without needing ad-hoc IAM decisions.
BigQuery pricing model: on-demand vs. capacity-based (reservations). Slot reservation sizing for predictable query workloads. Query cost governance: project-level and user-level cost controls. BI Engine reservation for Looker and Looker Studio query acceleration.
IAM model, column-level security policy tag taxonomy, and row-level security design — documented and reviewed with the security and compliance stakeholders before any data is loaded.
Warehouse provisioned against the architecture document. Representative data loaded and query performance validated against targets. Cost estimates verified against actual query patterns.