Amazon S3 → PlanetScale
AI-first ETL from Amazon S3 into PlanetScale. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon S3 into PlanetScale
Datrise syncs 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 S3: SaaS or API data source for analytics and warehouse sync.
PlanetScale: Serverless MySQL platform with safe schema workflows.
How Amazon S3 entities map to PlanetScale
| Amazon S3 entity | PlanetScale object | Notes |
|---|---|---|
| records | s3_records | id PK · custom fields → JSON columns |
| events | s3_events | DATETIME events |
| configuration objects | s3_configuration_objects | id PK · linked to s3_records |
FAQ
How does Datrise handle 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 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
More destinations for Amazon S3
More sources for PlanetScale
Early access
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