Google Cloud SQL to Azure Synapse Integration

How Datrise loads Google Cloud SQL into Azure Synapse

Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes into Azure Synapse as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.

Sync is incremental: Datrise uses COPY into staging, then a MERGE on stable id, so re-runs update only what changed. Hash distribution on the join id with date partitioning on facts. Synapse dedicated pools reward good hash-distribution choices, so Datrise distributes on entity ids to avoid data-movement-heavy joins.

Ideal for Azure analytics estates feeding Power BI.

Endpoints

Google Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.

Azure Synapse: Microsoft analytics workspace with SQL pools.

How Google Cloud SQL entities map to Azure Synapse

Google Cloud SQL entity	Azure Synapse object	Notes
tables	`google_cloud_sql_tables`	id PK · custom fields → NVARCHAR(MAX) JSON 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 Azure Synapse?

Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Synapse types.

How does the Google Cloud SQL to Azure Synapse sync stay up to date?

It runs incrementally — Datrise uses COPY into staging, then a MERGE on stable id.

Related pipelines

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