DatriseAI-first ETL

New York Times Spotfire

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

How Datrise loads New York Times into Spotfire

Datrise syncs New York Times's records, events, and configuration objects into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

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

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How New York Times entities map to Spotfire

New York Times entitySpotfire objectNotes
recordsnew_york_times_recordsid PK · custom fields → flattened columns for visualizations
eventsnew_york_times_eventsdate/time columns 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 Spotfire?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

Early access

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