Salesflare → DuckDB
AI-first ETL from Salesflare into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesflare into DuckDB
Datrise syncs Salesflare's B2B account intelligence, interactions, and automated 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
Salesflare: B2B CRM focused on automation and relationship timeline data.
DuckDB: In-process analytics database for fast local OLAP.
How Salesflare entities map to DuckDB
| Salesflare entity | DuckDB object | Notes |
|---|---|---|
| B2B account intelligence | salesflare_b2b_account_intelligence | id PK · custom fields → JSON or STRUCT columns |
| interactions | salesflare_interactions | id PK · linked to salesflare_b2b_account_intelligence |
| automated relationship timelines | salesflare_automated_relationship_timelines | TIMESTAMP WITH TIME ZONE events |
FAQ
How does Datrise handle Salesflare'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 Salesflare 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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