DatriseAI-first ETL

Hubplanner to Amazon Redshift Integration

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

How Datrise loads Hubplanner into Amazon Redshift

Datrise syncs Hubplanner's bookings, resources, projects, clients, timesheets, and utilization rates into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Hubplanner: Resource planning and utilization for agencies.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Hubplanner entities map to Amazon Redshift

Hubplanner entityAmazon Redshift objectNotes
bookingshubplanner_bookingsid PK · custom fields → SUPER 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 Amazon Redshift?

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

How does the Hubplanner to Amazon Redshift sync stay up to date?

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

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