Use case — Google BigQuery (coming soon)
BigQuery without a model, that's wasted compute.
BigQuery is serverless, fast, and scalable. But without modeling discipline, the platform fills with work tables, stacked views, and costly queries, driving up Google Cloud bills without improving data reliability. beVault generates native BigQuery SQL that leverages the platform's native mechanisms — partitioning, clustering, incremental ingestion.
- Native SQL BigQuery — automatic partitioning and clustering generated
- Native historization, without full reloads that consume slots
- Governed marts exposed directly to Looker Studio, Power BI or Tableau
- Natural integration into the Google Cloud ecosystem
Challenges
The four pitfalls of an unstructured BigQuery
Dataset proliferation
Each team creates its own work tables. Storage and query costs increase with no visibility into what is useful.
Unpredictable costs
Unoptimized queries on unpartitioned tables drive up slot consumption without warning.
Missing history
Without historization, data is overwritten with every load. Reconstructing the state of the data three months ago becomes impossible.
Partial governance
IAM manages access but says nothing about data quality or which table is authoritative for which indicator.
What beVault provides
A controlled BigQuery trajectory
- 01
Raw Vault historized on BigQuery
Incremental loading with change detection. No more full reloads wasting slots.
- 02
SQL optimized for BigQuery
Partitioning by load date, clustering by business key, optimized MERGE operations — the code leverages native mechanisms.
- 03
Integrated orchestration
Loads are orchestrated by beVault from the dependency graph. No external scheduler to maintain in your GCP environment.
- 04
Readable marts and governance
Documented restitution layer with full lineage and quality indicators, directly exposed to Looker Studio or any BI tool.
Frequently asked questions
BigQuery and beVault
Should we start from scratch on BigQuery?
No. We structure domain by domain, alongside the existing setup, with reconciliation before cutover.
Is Google BigQuery already supported?
Not yet. Snowflake, Amazon Redshift, Microsoft SQL Server and PostgreSQL are supported today; Google BigQuery is coming soon. Contact us to discuss your schedule.
Does beVault integrate with Dataform or dbt on GCP?
On the Data Vault layer and the marts, beVault handles generation and orchestration. If dbt or Dataform manage other GCP flows, the two coexist.
Do I own the generated code?
Yes. It's standard BigQuery SQL, readable, commented and exportable.
Next step