DatriseAI-first ETL

Google BigQuery to Google BigQuery Integration

AI-first ETL from Google BigQuery into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google BigQuery into Google BigQuery

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Google BigQuery: Serverless analytics warehouse on Google Cloud.

Google BigQuery: Serverless analytics warehouse on GCP.

How Google BigQuery entities map to Google BigQuery

Google BigQuery entityGoogle BigQuery objectNotes
datasetsbigquery_datasetsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
tablesbigquery_tablesid PK · linked to bigquery_datasets
viewsbigquery_viewsid PK · linked to bigquery_datasets
query jobsbigquery_query_jobsid PK · linked to bigquery_datasets

FAQ

How does Datrise handle Google BigQuery's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

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

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

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Early access

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