Google BigQuery to Spotfire Integration

How Datrise loads Google BigQuery into Spotfire

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Google BigQuery entities map to Spotfire

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

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

Early access

Connect Google BigQuery to Spotfire the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.