DatriseAI-first ETL

Harvest Forecast PlanetScale

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

How Datrise loads Harvest Forecast into PlanetScale

Datrise syncs Harvest Forecast'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

Harvest Forecast: SaaS or API data source for analytics and warehouse sync.

PlanetScale: Serverless MySQL platform with safe schema workflows.

How Harvest Forecast entities map to PlanetScale

Harvest Forecast entityPlanetScale objectNotes
recordsharvest_forecast_recordsid PK · custom fields → JSON columns
eventsharvest_forecast_eventsDATETIME events
configuration objectsharvest_forecast_configuration_objectsid PK · linked to harvest_forecast_records

FAQ

How does Datrise handle Harvest Forecast'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 Harvest Forecast 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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