Google BigQuery to Chartio Integration

How Datrise loads Google BigQuery into Chartio

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.

Ideal for drag-and-drop charting over a database.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Google BigQuery entities map to Chartio

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

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

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

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

Related pipelines

Early access

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