MongoDB → Mode
AI-first ETL from MongoDB into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads MongoDB into Mode
Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
MongoDB: Document database often used as an operational source for analytics.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How MongoDB entities map to Mode
| MongoDB entity | Mode object | Notes |
|---|---|---|
| collections | mongodb_collections | id PK · custom fields → flattened columns for SQL and notebooks |
| documents | mongodb_documents | id PK · linked to mongodb_collections |
| change streams | mongodb_change_streams | temporal columns events |
| schema snapshots | mongodb_schema_snapshots | id PK · linked to mongodb_collections |
FAQ
How does Datrise handle MongoDB's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the MongoDB to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for MongoDB
Early access
Connect MongoDB to Mode the easy way
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