Particle to Chartio Integration
AI-first ETL from Particle into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Particle into Chartio
Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
Particle: IoT device fleet telemetry and events.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How Particle entities map to Chartio
| Particle entity | Chartio object | Notes |
|---|---|---|
| devices | particle_devices | id PK · custom fields → flattened columns for visual SQL |
| events | particle_events | temporal 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 Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the Particle to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Particle
Early access
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