DatriseAI-first ETL

MongoDB Google BigQuery

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

How Datrise loads MongoDB into Google BigQuery

Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots 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

MongoDB: Document database often used as an operational source for analytics.

Google BigQuery: Serverless analytics warehouse on GCP.

How MongoDB entities map to Google BigQuery

MongoDB entityGoogle BigQuery objectNotes
collectionsmongodb_collectionsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
documentsmongodb_documentsid PK · linked to mongodb_collections
change streamsmongodb_change_streamsTIMESTAMP events
schema snapshotsmongodb_schema_snapshotsid PK · linked to mongodb_collections

FAQ

How does Datrise handle MongoDB'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 MongoDB 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.

Related pipelines

Early access

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