DatriseAI-first ETL

Import API to DuckDB Integration

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

How Datrise loads Import API into DuckDB

Datrise syncs Import API's records, payloads, batches, schemas, ingest timestamps, and source keys 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

Import API: Direct API push of custom records into a pipeline.

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

How Import API entities map to DuckDB

Import API entityDuckDB objectNotes
recordsimport_api_recordsid PK · custom fields → JSON or STRUCT columns
payloadsimport_api_payloadsid PK · linked to import_api_records
batchesimport_api_batchesid PK · linked to import_api_records
schemasimport_api_schemasid PK · linked to import_api_records

FAQ

How does Datrise handle Import API'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 Import API 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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