Particle to DuckDB Integration

How Datrise loads Particle into DuckDB

Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Particle: IoT device fleet telemetry and events.

DuckDB: In-process analytics database for fast local OLAP.

How Particle entities map to DuckDB

Particle entity	DuckDB object	Notes
devices	`particle_devices`	id PK · custom fields → JSON or STRUCT columns
events	`particle_events`	TIMESTAMP WITH TIME ZONE 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 DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

How does the Particle to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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