Keap → Google BigQuery
AI-first ETL from Keap into Google BigQuery. Governed entities, incremental sync, typed landing tables.
How Datrise loads Keap into Google BigQuery
Datrise syncs Keap's SMB contacts, opportunities, automations, and appointment workflows 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
Keap: SMB CRM with pipeline automation, email, and appointment flows.
Google BigQuery: Serverless analytics warehouse on GCP.
How Keap entities map to Google BigQuery
| Keap entity | Google BigQuery object | Notes |
|---|---|---|
| SMB contacts | keap_smb_contacts | id PK · custom fields → JSON or nested/repeated (STRUCT) columns |
| opportunities | keap_opportunities | id PK · linked to keap_smb_contacts |
| automations | keap_automations | id PK · linked to keap_smb_contacts |
| appointment workflows | keap_appointment_workflows | id PK · linked to keap_smb_contacts |
FAQ
How does Datrise handle Keap'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 Keap 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
Connect Keap to Google BigQuery the easy way
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