Google BigQuery to Birst Integration

How Datrise loads Google BigQuery into Birst

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.

Ideal for networked, governed enterprise BI.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Birst: Cloud BI with networked analytics and enterprise semantic layers.

How Google BigQuery entities map to Birst

Google BigQuery entity	Birst object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → flattened columns
tables	`bigquery_tables`	id PK · linked to bigquery_datasets
views	`bigquery_views`	id PK · linked to bigquery_datasets
query jobs	`bigquery_query_jobs`	id PK · linked to bigquery_datasets

FAQ

How does Datrise handle Google BigQuery's custom fields in Birst?

Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.

How does the Google BigQuery to Birst sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.

Related pipelines

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