Copper → DuckDB
AI-first ETL from Copper into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Copper into DuckDB
Datrise syncs Copper's Google Workspace CRM entities, opportunities, and relationship timelines 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
Copper: Google Workspace-native CRM.
DuckDB: In-process analytics database for fast local OLAP.
How Copper entities map to DuckDB
| Copper entity | DuckDB object | Notes |
|---|---|---|
| Google Workspace CRM entities | copper_google_workspace_crm_entities | id PK · custom fields → JSON or STRUCT columns |
| opportunities | copper_opportunities | id PK · linked to copper_google_workspace_crm_entities |
| relationship timelines | copper_relationship_timelines | TIMESTAMP WITH TIME ZONE events |
FAQ
How does Datrise handle Copper'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 Copper 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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