DatriseAI-first ETL

Salesloft Snowflake

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

How Datrise loads Salesloft into Snowflake

Datrise syncs Salesloft's cadence activity, conversation signals, and revenue workflow execution 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

Salesloft: Revenue workflow platform for cadences, conversations, and coaching signals.

Snowflake: Cloud data warehouse with separated compute and storage.

How Salesloft entities map to Snowflake

Salesloft entitySnowflake objectNotes
cadence activitysalesloft_cadence_activityTIMESTAMP_TZ events
conversation signalssalesloft_conversation_signalsid PK · linked to salesloft_cadence_activity
revenue workflow executionsalesloft_revenue_workflow_executionid PK · linked to salesloft_cadence_activity

FAQ

How does Datrise handle Salesloft'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 Salesloft 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.

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