DatriseAI-first ETL

Amazon Amazon S3 Supabase

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

How Datrise loads Amazon Amazon S3 into Supabase

Datrise syncs Amazon Amazon S3's records, events, and configuration objects into Supabase as a typed table per source entity in your Supabase Postgres. 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 updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning for high-volume tables. Datrise lands into a dedicated schema and leaves row-level security to you, so synced tables don't inherit public access by accident.

Ideal for app builders who want CRM data alongside their Supabase product data.

Endpoints

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

Supabase: Postgres platform with auth, storage, and realtime APIs.

How Amazon Amazon S3 entities map to Supabase

Amazon Amazon S3 entitySupabase objectNotes
recordsamazon_s3_recordsid PK · custom fields → jsonb columns
eventsamazon_s3_eventstimestamptz events
configuration objectsamazon_s3_configuration_objectsid PK · linked to amazon_s3_records

FAQ

How does Datrise handle Amazon Amazon S3's custom fields in Supabase?

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 Supabase types.

How does the Amazon Amazon S3 to Supabase sync stay up to date?

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

Related pipelines

Early access

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