DatriseAI-first ETL

New York Times DuckDB

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

How Datrise loads New York Times into DuckDB

Datrise syncs New York Times's records, events, and configuration objects 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

New York Times: SaaS or API data source for analytics and warehouse sync.

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

How New York Times entities map to DuckDB

New York Times entityDuckDB objectNotes
recordsnew_york_times_recordsid PK · custom fields → JSON or STRUCT columns
eventsnew_york_times_eventsTIMESTAMP WITH TIME ZONE events
configuration objectsnew_york_times_configuration_objectsid PK · linked to new_york_times_records

FAQ

How does Datrise handle New York Times'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 New York Times 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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