DatriseAI-first ETL

MongoDB ThoughtSpot

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

How Datrise loads MongoDB into ThoughtSpot

Datrise syncs MongoDB's collections, documents, change streams, and schema snapshots into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.

Ideal for natural-language search analytics over a warehouse.

Endpoints

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

ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.

How MongoDB entities map to ThoughtSpot

MongoDB entityThoughtSpot objectNotes
collectionsmongodb_collectionsid PK · custom fields → flattened columns for searchable fields
documentsmongodb_documentsid PK · linked to mongodb_collections
change streamsmongodb_change_streamsdate/time columns events
schema snapshotsmongodb_schema_snapshotsid PK · linked to mongodb_collections

FAQ

How does Datrise handle MongoDB's custom fields in ThoughtSpot?

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

How does the MongoDB to ThoughtSpot sync stay up to date?

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

Related pipelines

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