Particle to Amazon Athena Integration

How Datrise loads Particle into Amazon Athena

Datrise syncs Particle's devices, events, variables, firmware versions, product fleets, and device logs into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Particle: IoT device fleet telemetry and events.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Particle entities map to Amazon Athena

Particle entity	Amazon Athena object	Notes
devices	`particle_devices`	id PK · custom fields → struct/map columns in Parquet
events	`particle_events`	timestamp 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 Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Particle to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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