DatriseAI-first ETL

Salesloft Google BigQuery

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

How Datrise loads Salesloft into Google BigQuery

Datrise syncs Salesloft's cadence activity, conversation signals, and revenue workflow execution into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Salesloft entities map to Google BigQuery

Salesloft entityGoogle BigQuery 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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Salesloft to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

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Early access

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