DatriseAI-first ETL

Public API to DuckDB Integration

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

How Datrise loads Public API into DuckDB

Datrise syncs Public API's records, response payloads, pagination cursors, request parameters, schemas, and fetch timestamps 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

Public API: Generic REST endpoint pulled on a schedule.

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

How Public API entities map to DuckDB

Public API entityDuckDB objectNotes
recordspublic_api_recordsid PK · custom fields → JSON or STRUCT columns
response payloadspublic_api_response_payloadsid PK · linked to public_api_records
pagination cursorspublic_api_pagination_cursorsid PK · linked to public_api_records
request parameterspublic_api_request_parametersid PK · linked to public_api_records

FAQ

How does Datrise handle Public 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 Public 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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