Freshdesk → Spotfire
AI-first ETL from Freshdesk into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshdesk into Spotfire
Datrise syncs Freshdesk's tickets, contacts, agents, SLA events, and satisfaction scores into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
Freshdesk: Customer support helpdesk with tickets, SLAs, and agent workflows.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How Freshdesk entities map to Spotfire
| Freshdesk entity | Spotfire object | Notes |
|---|---|---|
| tickets | freshdesk_tickets | id PK · custom fields → flattened columns for visualizations |
| contacts | freshdesk_contacts | id PK · linked to freshdesk_tickets |
| agents | freshdesk_agents | id PK · linked to freshdesk_tickets |
| SLA events | freshdesk_sla_events | date/time columns events |
FAQ
How does Datrise handle Freshdesk's custom fields in Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the Freshdesk to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
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