DatriseAI-first ETL

Hp Postgres Yellowfin

AI-first ETL from Hp Postgres into Yellowfin. Governed entities, incremental sync, typed landing tables.

How Datrise loads Hp Postgres into Yellowfin

Datrise syncs Hp Postgres's records, events, and configuration objects into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.

Ideal for dashboards with automated data storytelling.

Endpoints

Hp Postgres: SaaS or API data source for analytics and warehouse sync.

Yellowfin: BI suite with dashboards, automated insights, and data storytelling.

How Hp Postgres entities map to Yellowfin

Hp Postgres entityYellowfin objectNotes
recordshp_postgres_recordsid PK · custom fields → flattened columns
eventshp_postgres_eventsdate/time dimensions events
configuration objectshp_postgres_configuration_objectsid PK · linked to hp_postgres_records

FAQ

How does Datrise handle Hp Postgres's custom fields in Yellowfin?

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

How does the Hp Postgres to Yellowfin sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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