DatriseAI-first ETL

Hubplanner to Snowflake Integration

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

How Datrise loads Hubplanner into Snowflake

Datrise syncs Hubplanner's bookings, resources, projects, clients, timesheets, and utilization rates 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

Hubplanner: Resource planning and utilization for agencies.

Snowflake: Cloud data warehouse with separated compute and storage.

How Hubplanner entities map to Snowflake

Hubplanner entitySnowflake objectNotes
bookingshubplanner_bookingsid PK · custom fields → VARIANT columns
resourceshubplanner_resourcesid PK · linked to hubplanner_bookings
projectshubplanner_projectsid PK · linked to hubplanner_bookings
clientshubplanner_clientsid PK · linked to hubplanner_bookings

FAQ

How does Datrise handle Hubplanner'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 Hubplanner 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

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