Freshsales → Birst
AI-first ETL from Freshsales into Birst. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshsales into Birst
Datrise syncs Freshsales's leads, contacts, deals, calls, and email activity telemetry into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.
Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.
Ideal for networked, governed enterprise BI.
Endpoints
Freshsales: CRM by Freshworks with built-in phone and email.
Birst: Cloud BI with networked analytics and enterprise semantic layers.
How Freshsales entities map to Birst
| Freshsales entity | Birst object | Notes |
|---|---|---|
| leads | freshsales_leads | id PK · custom fields → flattened columns |
| contacts | freshsales_contacts | id PK · linked to freshsales_leads |
| deals | freshsales_deals | id PK · linked to freshsales_leads |
| calls | freshsales_calls | id PK · linked to freshsales_leads |
FAQ
How does Datrise handle Freshsales's custom fields in Birst?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Birst types.
How does the Freshsales to Birst sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.
Related pipelines
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