Chorus.ai → DuckDB
AI-first ETL from Chorus.ai into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Chorus.ai into DuckDB
Datrise syncs Chorus.ai's contacts, accounts, deals, activities, and lifecycle events 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
Chorus.ai: Revenue intelligence for conversation insights and forecast accuracy.
DuckDB: In-process analytics database for fast local OLAP.
How Chorus.ai entities map to DuckDB
| Chorus.ai entity | DuckDB object | Notes |
|---|---|---|
| contacts | chorus_contacts | id PK · custom fields → JSON or STRUCT columns |
| accounts | chorus_accounts | id PK · linked to chorus_contacts |
| deals | chorus_deals | id PK · linked to chorus_contacts |
| activities | chorus_activities | TIMESTAMP WITH TIME ZONE events |
FAQ
How does Datrise handle Chorus.ai'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 Chorus.ai 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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