Impartner to DuckDB Integration
AI-first ETL from Impartner into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Impartner into DuckDB
Datrise syncs Impartner's partners, deal registrations, opportunities, tiers, MDF requests, and partner contacts 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
Impartner: Channel management for partner tiers and co-sell.
DuckDB: In-process analytics database for fast local OLAP.
How Impartner entities map to DuckDB
| Impartner entity | DuckDB object | Notes |
|---|---|---|
| partners | impartner_partners | id PK · custom fields → JSON or STRUCT columns |
| deal registrations | impartner_deal_registrations | id PK · linked to impartner_partners |
| opportunities | impartner_opportunities | id PK · linked to impartner_partners |
| tiers | impartner_tiers | id PK · linked to impartner_partners |
FAQ
How does Datrise handle Impartner'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 Impartner 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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Early access
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