DatriseAI-first ETL

Amazon Amazon S3 PlanetScale

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

How Datrise loads Amazon Amazon S3 into PlanetScale

Datrise syncs Amazon Amazon S3's records, events, and configuration objects into PlanetScale as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Vitess sharding by tenant or entity key for very large tables. PlanetScale disallows foreign-key constraints by default, so Datrise models relationships by stable id columns rather than enforced FKs.

Ideal for horizontally scalable MySQL apps on Vitess.

Endpoints

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

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Amazon Amazon S3 entities map to PlanetScale

Amazon Amazon S3 entityPlanetScale objectNotes
recordsamazon_s3_recordsid PK · custom fields → JSON columns
eventsamazon_s3_eventsDATETIME events
configuration objectsamazon_s3_configuration_objectsid PK · linked to amazon_s3_records

FAQ

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

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

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

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

Related pipelines

Early access

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