Google Cloud SQL to Chartio Integration

How Datrise loads Google Cloud SQL into Chartio

Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes 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 Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.

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

How Google Cloud SQL entities map to Chartio

Google Cloud SQL entity	Chartio object	Notes
tables	`google_cloud_sql_tables`	id PK · custom fields → flattened columns for visual SQL
rows	`google_cloud_sql_rows`	id PK · linked to google_cloud_sql_tables
schemas	`google_cloud_sql_schemas`	id PK · linked to google_cloud_sql_tables
indexes	`google_cloud_sql_indexes`	id PK · linked to google_cloud_sql_tables

FAQ

How does Datrise handle Google Cloud SQL'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 Cloud SQL to Chartio sync stay up to date?

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

Related pipelines

Early access

Connect Google Cloud SQL to Chartio 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.