DatriseAI-first ETL

VinSolutions to DuckDB Integration

AI-first ETL from VinSolutions into DuckDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads VinSolutions into DuckDB

Datrise syncs VinSolutions's leads, customers, vehicles, deals, appointments, and service records 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

VinSolutions: Dealer CRM tied to inventory and equity mining.

DuckDB: In-process analytics database for fast local OLAP.

How VinSolutions entities map to DuckDB

VinSolutions entityDuckDB objectNotes
leadsvinsolutions_leadsid PK · custom fields → JSON or STRUCT columns
customersvinsolutions_customersid PK · linked to vinsolutions_leads
vehiclesvinsolutions_vehiclesid PK · linked to vinsolutions_leads
dealsvinsolutions_dealsid PK · linked to vinsolutions_leads

FAQ

How does Datrise handle VinSolutions'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 VinSolutions to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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