DatriseAI-first ETL

New York Times Snowflake

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

How Datrise loads New York Times into Snowflake

Datrise syncs New York Times's records, events, and configuration objects into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

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

Snowflake: Cloud data warehouse with separated compute and storage.

How New York Times entities map to Snowflake

New York Times entitySnowflake objectNotes
recordsnew_york_times_recordsid PK · custom fields → VARIANT columns
eventsnew_york_times_eventsTIMESTAMP_TZ 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 Snowflake?

Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.

How does the New York Times to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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