MongoDB → Sisense
AI-first ETL from MongoDB into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads MongoDB into Sisense
Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
MongoDB: Document database often used as an operational source for analytics.
Sisense: Analytics platform with elastic data models and embedded analytics.
How MongoDB entities map to Sisense
| MongoDB entity | Sisense object | Notes |
|---|---|---|
| collections | mongodb_collections | id PK · custom fields → flattened columns for the cube |
| documents | mongodb_documents | id PK · linked to mongodb_collections |
| change streams | mongodb_change_streams | date/time fields events |
| schema snapshots | mongodb_schema_snapshots | id PK · linked to mongodb_collections |
FAQ
How does Datrise handle MongoDB's custom fields in Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the MongoDB to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for MongoDB
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