Google BigQuery to Yellowfin Integration

How Datrise loads Google BigQuery into Yellowfin

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Yellowfin as warehouse tables Yellowfin builds views on. 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 connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.

Ideal for dashboards with automated data storytelling.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Yellowfin: BI suite with dashboards, automated insights, and data storytelling.

How Google BigQuery entities map to Yellowfin

Google BigQuery entity	Yellowfin 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 Yellowfin?

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 Yellowfin types.

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

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

Related pipelines

Early access

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