Google BigQuery to Apache Superset Integration

How Datrise loads Google BigQuery into Apache Superset

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Apache Superset as governed SQL tables Superset queries directly. Flexible or custom fields land in flattened columns for the explore UI, and timestamps such as created, updated, and status changes are typed as temporal columns for time-series charts.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned tables to keep dashboards responsive. Superset charts run live SQL, so Datrise lands query-friendly, indexed tables rather than wide raw payloads.

Ideal for open-source dashboards over your own database.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Apache Superset: Open-source BI for SQL exploration, charts, and dashboard publishing.

How Google BigQuery entities map to Apache Superset

Google BigQuery entity	Apache Superset object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → flattened columns for the explore UI
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 Apache Superset?

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

How does the Google BigQuery to Apache Superset sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the queried tables.

Related pipelines

Early access

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