DatriseAI-first ETL

US Census PostgreSQL

AI-first ETL from US Census into PostgreSQL. Governed entities, incremental sync, typed landing tables.

How Datrise loads US Census into PostgreSQL

Datrise syncs US Census's records, events, and configuration objects into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

US Census: SaaS or API data source for analytics and warehouse sync.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How US Census entities map to PostgreSQL

US Census entityPostgreSQL objectNotes
recordsus_census_recordsid PK · custom fields → jsonb columns
eventsus_census_eventstimestamptz events
configuration objectsus_census_configuration_objectsid PK · linked to us_census_records

FAQ

How does Datrise handle US Census's custom fields in PostgreSQL?

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

How does the US Census to PostgreSQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

Early access

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