DatriseAI-first ETL

Amazon S3 PostgreSQL

AI-first ETL from Amazon S3 into PostgreSQL. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon S3 into PostgreSQL

Datrise syncs Amazon S3'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

Amazon S3: SaaS or API data source for analytics and warehouse sync.

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

How Amazon S3 entities map to PostgreSQL

Amazon S3 entityPostgreSQL objectNotes
recordss3_recordsid PK · custom fields → jsonb columns
eventss3_eventstimestamptz events
configuration objectss3_configuration_objectsid PK · linked to s3_records

FAQ

How does Datrise handle Amazon S3'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 Amazon S3 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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