Freshdesk → PlanetScale
AI-first ETL from Freshdesk into PlanetScale. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshdesk into PlanetScale
Datrise syncs Freshdesk's tickets, contacts, agents, SLA events, and satisfaction scores 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
Freshdesk: Customer support helpdesk with tickets, SLAs, and agent workflows.
PlanetScale: Serverless MySQL platform with safe schema workflows.
How Freshdesk entities map to PlanetScale
| Freshdesk entity | PlanetScale object | Notes |
|---|---|---|
| tickets | freshdesk_tickets | id PK · custom fields → JSON columns |
| contacts | freshdesk_contacts | id PK · linked to freshdesk_tickets |
| agents | freshdesk_agents | id PK · linked to freshdesk_tickets |
| SLA events | freshdesk_sla_events | DATETIME events |
FAQ
How does Datrise handle Freshdesk'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 Freshdesk 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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