DatriseAI-first ETL

Freshsales Redash

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

How Datrise loads Freshsales into Redash

Datrise syncs Freshsales's leads, contacts, deals, calls, and email activity telemetry into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.

Ideal for lightweight, query-driven dashboards.

Endpoints

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

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How Freshsales entities map to Redash

Freshsales entityRedash objectNotes
leadsfreshsales_leadsid PK · custom fields → flattened columns for query results
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 Redash?

Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.

How does the Freshsales to Redash sync stay up to date?

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

Related pipelines

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