Particle to Neon Integration
AI-first ETL from Particle into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Particle into Neon
Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into Neon as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.
Ideal for serverless Postgres workloads that scale to zero between syncs.
Endpoints
Particle: IoT device fleet telemetry and events.
Neon: Serverless Postgres destination with branching and autoscaling.
How Particle entities map to Neon
| Particle entity | Neon object | Notes |
|---|---|---|
| devices | particle_devices | id PK · custom fields → jsonb columns |
| events | particle_events | timestamptz 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 Neon?
Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Neon types.
How does the Particle to Neon sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
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