Salesflare → Google BigQuery
AI-first ETL from Salesflare into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesflare into Google BigQuery
Datrise syncs Salesflare's B2B account intelligence, interactions, and automated relationship timelines 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
Salesflare: B2B CRM focused on automation and relationship timeline data.
Google BigQuery: Serverless analytics warehouse on GCP.
How Salesflare entities map to Google BigQuery
| Salesflare entity | Google BigQuery object | Notes |
|---|---|---|
| B2B account intelligence | salesflare_b2b_account_intelligence | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| interactions | salesflare_interactions | id PK · linked to salesflare_b2b_account_intelligence |
| automated relationship timelines | salesflare_automated_relationship_timelines | TIMESTAMP events |
FAQ
How does Datrise handle Salesflare'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 Salesflare 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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