Keap → Looker
AI-first ETL from Keap into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Keap into Looker
Datrise syncs Keap's SMB contacts, opportunities, automations, and appointment workflows 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
Keap: SMB CRM with pipeline automation, email, and appointment flows.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Keap entities map to Looker
| Keap entity | Looker object | Notes |
|---|---|---|
| SMB contacts | keap_smb_contacts | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| 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 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 Keap to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
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