Salesloft → Looker
AI-first ETL from Salesloft into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesloft into Looker
Datrise syncs Salesloft's cadence activity, conversation signals, and revenue workflow execution into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Salesloft: Revenue workflow platform for cadences, conversations, and coaching signals.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Salesloft entities map to Looker
| Salesloft entity | Looker object | Notes |
|---|---|---|
| cadence activity | salesloft_cadence_activity | date/time dimension columns events |
| conversation signals | salesloft_conversation_signals | id PK · linked to salesloft_cadence_activity |
| revenue workflow execution | salesloft_revenue_workflow_execution | id PK · linked to salesloft_cadence_activity |
FAQ
How does Datrise handle Salesloft's custom fields in Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Salesloft to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for Salesloft
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