Freshdesk → Mode
AI-first ETL from Freshdesk into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshdesk into Mode
Datrise syncs Freshdesk's tickets, contacts, agents, SLA events, and satisfaction scores into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Freshdesk: Customer support helpdesk with tickets, SLAs, and agent workflows.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Freshdesk entities map to Mode
| Freshdesk entity | Mode object | Notes |
|---|---|---|
| tickets | freshdesk_tickets | id PK · custom fields → flattened columns for SQL and notebooks |
| contacts | freshdesk_contacts | id PK · linked to freshdesk_tickets |
| agents | freshdesk_agents | id PK · linked to freshdesk_tickets |
| SLA events | freshdesk_sla_events | temporal columns events |
FAQ
How does Datrise handle Freshdesk's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Freshdesk to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
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