Hubplanner to DuckDB Integration
AI-first ETL from Hubplanner into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Hubplanner into DuckDB
Datrise syncs Hubplanner's bookings, resources, projects, clients, timesheets, and utilization rates into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
Hubplanner: Resource planning and utilization for agencies.
DuckDB: In-process analytics database for fast local OLAP.
How Hubplanner entities map to DuckDB
| Hubplanner entity | DuckDB object | Notes |
|---|---|---|
| bookings | hubplanner_bookings | id PK · custom fields → JSON or STRUCT columns |
| resources | hubplanner_resources | id PK · linked to hubplanner_bookings |
| projects | hubplanner_projects | id PK · linked to hubplanner_bookings |
| clients | hubplanner_clients | id PK · linked to hubplanner_bookings |
FAQ
How does Datrise handle Hubplanner's custom fields in DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the Hubplanner to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
Related pipelines
More destinations for Hubplanner
Early access
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