DatriseAI-first ETL

Onfleet to Snowflake Integration

AI-first ETL from Onfleet into Snowflake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Onfleet into Snowflake

Datrise syncs Onfleet's tasks, workers, teams, destinations, delivery events, and recipients into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Onfleet: Last-mile delivery dispatch and driver tracking.

Snowflake: Cloud data warehouse with separated compute and storage.

How Onfleet entities map to Snowflake

Onfleet entitySnowflake objectNotes
tasksonfleet_tasksid PK · custom fields → VARIANT columns
workersonfleet_workersid PK · linked to onfleet_tasks
teamsonfleet_teamsid PK · linked to onfleet_tasks
destinationsonfleet_destinationsid PK · linked to onfleet_tasks

FAQ

How does Datrise handle Onfleet's custom fields in Snowflake?

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

How does the Onfleet to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

Early access

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