Particle to ThoughtSpot Integration
AI-first ETL from Particle into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Particle into ThoughtSpot
Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs 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
Particle: IoT device fleet telemetry and events.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Particle entities map to ThoughtSpot
| Particle entity | ThoughtSpot object | Notes |
|---|---|---|
| devices | particle_devices | id PK · custom fields → flattened columns for searchable fields |
| events | particle_events | date/time columns events |
| variables | particle_variables | id PK · linked to particle_devices |
| firmware versions | particle_firmware_versions | id PK · linked to particle_devices |
FAQ
How does Datrise handle Particle'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 Particle to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
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Early access
Connect Particle to ThoughtSpot the easy way
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