DatriseAI-first ETL

Salesloft Databricks SQL Warehouse

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

How Datrise loads Salesloft into Databricks SQL Warehouse

Datrise syncs Salesloft's cadence activity, conversation signals, and revenue workflow execution into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

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

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Salesloft entities map to Databricks SQL Warehouse

Salesloft entityDatabricks SQL Warehouse objectNotes
cadence activitysalesloft_cadence_activityTIMESTAMP 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 Databricks SQL Warehouse?

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

How does the Salesloft to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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