Particle to Looker Studio Integration
AI-first ETL from Particle into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Particle into Looker Studio
Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Particle: IoT device fleet telemetry and events.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Particle entities map to Looker Studio
| Particle entity | Looker Studio object | Notes |
|---|---|---|
| devices | particle_devices | id PK · custom fields → flattened columns for chart fields |
| events | particle_events | date dimension 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 Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Particle to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect Particle to Looker Studio the easy way
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