DatriseAI-first ETL

Apollo PlanetScale

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

How Datrise loads Apollo into PlanetScale

Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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

Apollo: Sales intelligence and engagement platform with account-level activity.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Apollo entities map to PlanetScale

Apollo entityPlanetScale objectNotes
sales intelligence recordsapollo_sales_intelligence_recordsid PK · custom fields → JSON columns
account engagementapollo_account_engagementid PK · linked to apollo_sales_intelligence_records
outbound activityapollo_outbound_activityDATETIME events

FAQ

How does Datrise handle Apollo'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 Apollo 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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