Google BigQuery to Looker Studio Integration

How Datrise loads Google BigQuery into Looker Studio

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Google BigQuery entities map to Looker Studio

Google BigQuery entity	Looker Studio object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → flattened columns for chart fields
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 Looker Studio?

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

How does the Google BigQuery to Looker Studio sync stay up to date?

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

Related pipelines

Early access

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