DatriseAI-first ETL

Freshsales Snowflake

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

How Datrise loads Freshsales into Snowflake

Datrise syncs Freshsales's leads, contacts, deals, calls, and email activity telemetry into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

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

Snowflake: Cloud data warehouse with separated compute and storage.

How Freshsales entities map to Snowflake

Freshsales entitySnowflake objectNotes
leadsfreshsales_leadsid PK · custom fields → VARIANT columns
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 Snowflake?

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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