DatriseAI-first ETL

Freshsales Looker Studio

AI-first ETL from Freshsales into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Freshsales into Looker Studio

Datrise syncs Freshsales's leads, contacts, deals, calls, and email activity telemetry 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

Freshsales: CRM by Freshworks with built-in phone and email.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Freshsales entities map to Looker Studio

Freshsales entityLooker Studio objectNotes
leadsfreshsales_leadsid PK · custom fields → flattened columns for chart fields
contactsfreshsales_contactsid PK · linked to freshsales_leads
dealsfreshsales_dealsid PK · linked to freshsales_leads
callsfreshsales_callsid PK · linked to freshsales_leads

FAQ

How does Datrise handle Freshsales'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 Freshsales to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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