Google BigQuery to Azure Synapse Integration

How Datrise loads Google BigQuery into Azure Synapse

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions 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 BigQuery: Serverless analytics warehouse on Google Cloud.

Azure Synapse: Microsoft analytics workspace with SQL pools.

How Google BigQuery entities map to Azure Synapse

Google BigQuery entity	Azure Synapse object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → NVARCHAR(MAX) JSON columns
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 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 BigQuery 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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