Allbound to DuckDB Integration
AI-first ETL from Allbound into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Allbound into DuckDB
Datrise syncs Allbound's partners, deal registrations, leads, content assets, learning tracks, and partner users 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
Allbound: Partner portal for enablement, content, and deal registration.
DuckDB: In-process analytics database for fast local OLAP.
How Allbound entities map to DuckDB
| Allbound entity | DuckDB object | Notes |
|---|---|---|
| partners | allbound_partners | id PK · custom fields → JSON or STRUCT columns |
| deal registrations | allbound_deal_registrations | id PK · linked to allbound_partners |
| leads | allbound_leads | id PK · linked to allbound_partners |
| content assets | allbound_content_assets | id PK · linked to allbound_partners |
FAQ
How does Datrise handle Allbound'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 Allbound to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
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