Keap → Mode
AI-first ETL from Keap into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Keap into Mode
Datrise syncs Keap's SMB contacts, opportunities, automations, and appointment workflows into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Keap: SMB CRM with pipeline automation, email, and appointment flows.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Keap entities map to Mode
| Keap entity | Mode object | Notes |
|---|---|---|
| SMB contacts | keap_smb_contacts | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Keap to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
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