DatriseAI-first ETL

MongoDB Looker Studio

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

How Datrise loads MongoDB into Looker Studio

Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

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

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How MongoDB entities map to Looker Studio

MongoDB entityLooker Studio objectNotes
collectionsmongodb_collectionsid PK · custom fields → flattened columns for chart fields
documentsmongodb_documentsid PK · linked to mongodb_collections
change streamsmongodb_change_streamsdate dimension columns events
schema snapshotsmongodb_schema_snapshotsid PK · linked to mongodb_collections

FAQ

How does Datrise handle MongoDB's custom fields in Looker Studio?

Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.

How does the MongoDB to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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