DatriseAI-first ETL

Apache Spark PlanetScale

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

How Datrise loads Apache Spark into PlanetScale

Datrise syncs Apache Spark'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

Apache Spark: SaaS or API data source for analytics and warehouse sync.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Apache Spark entities map to PlanetScale

Apache Spark entityPlanetScale objectNotes
recordsapache_spark_recordsid PK · custom fields → JSON columns
eventsapache_spark_eventsDATETIME events
configuration objectsapache_spark_configuration_objectsid PK · linked to apache_spark_records

FAQ

How does Datrise handle Apache Spark'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 Apache Spark 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

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