Google BigQuery to Tableau Integration

How Datrise loads Google BigQuery into Tableau

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Google BigQuery entities map to Tableau

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

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

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

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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