DatriseAI-first ETL

Hubplanner to Databricks SQL Warehouse Integration

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

How Datrise loads Hubplanner into Databricks SQL Warehouse

Datrise syncs Hubplanner's bookings, resources, projects, clients, timesheets, and utilization rates into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Hubplanner: Resource planning and utilization for agencies.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Hubplanner entities map to Databricks SQL Warehouse

Hubplanner entityDatabricks SQL Warehouse objectNotes
bookingshubplanner_bookingsid PK · custom fields → VARIANT or STRUCT 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 Databricks SQL Warehouse?

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

How does the Hubplanner to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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