Google Cloud SQL to Yellowfin Integration

How Datrise loads Google Cloud SQL into Yellowfin

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

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

How Google Cloud SQL entities map to Yellowfin

Google Cloud SQL entity	Yellowfin object	Notes
tables	`google_cloud_sql_tables`	id PK · custom fields → flattened columns
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 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 Cloud SQL to Yellowfin 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 Yellowfin the easy way

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