Google BigQuery to Amazon Redshift Integration

How Datrise loads Google BigQuery into Amazon Redshift

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Google BigQuery entities map to Amazon Redshift

Google BigQuery entity	Amazon Redshift object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → SUPER 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 Amazon Redshift?

Flexible values are stored as SUPER columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Redshift types.

How does the Google BigQuery to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

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