Freshdesk → Looker Studio
AI-first ETL from Freshdesk into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshdesk into Looker Studio
Datrise syncs Freshdesk's tickets, contacts, agents, SLA events, and satisfaction scores into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Freshdesk: Customer support helpdesk with tickets, SLAs, and agent workflows.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Freshdesk entities map to Looker Studio
| Freshdesk entity | Looker Studio object | Notes |
|---|---|---|
| tickets | freshdesk_tickets | id PK · custom fields → flattened columns for chart fields |
| contacts | freshdesk_contacts | id PK · linked to freshdesk_tickets |
| agents | freshdesk_agents | id PK · linked to freshdesk_tickets |
| SLA events | freshdesk_sla_events | date dimension columns events |
FAQ
How does Datrise handle Freshdesk's custom fields in Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Freshdesk to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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