MongoDB → Spotfire
AI-first ETL from MongoDB into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads MongoDB into Spotfire
Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
MongoDB: Document database often used as an operational source for analytics.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How MongoDB entities map to Spotfire
| MongoDB entity | Spotfire object | Notes |
|---|---|---|
| collections | mongodb_collections | id PK · custom fields → flattened columns for visualizations |
| documents | mongodb_documents | id PK · linked to mongodb_collections |
| change streams | mongodb_change_streams | date/time columns events |
| schema snapshots | mongodb_schema_snapshots | id PK · linked to mongodb_collections |
FAQ
How does Datrise handle MongoDB's custom fields in Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the MongoDB to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
Related pipelines
More destinations for MongoDB
Early access
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