Particle to Birst Integration
AI-first ETL from Particle into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Particle into Birst
Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
Particle: IoT device fleet telemetry and events.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Particle entities map to Birst
| Particle entity | Birst object | Notes |
|---|---|---|
| devices | particle_devices | id PK · custom fields → flattened columns |
| events | particle_events | date/time dimensions 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 Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the Particle to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
More destinations for Particle
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