Mautic → Mode
AI-first ETL from Mautic into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mautic into Mode
Datrise syncs Mautic's contacts, accounts, deals, activities, and lifecycle events 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
Mautic: Open-source CRM for customizable sales and customer workflows.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Mautic entities map to Mode
| Mautic entity | Mode object | Notes |
|---|---|---|
| contacts | mautic_contacts | id PK · custom fields → flattened columns for SQL and notebooks |
| accounts | mautic_accounts | id PK · linked to mautic_contacts |
| deals | mautic_deals | id PK · linked to mautic_contacts |
| activities | mautic_activities | temporal columns events |
FAQ
How does Datrise handle Mautic'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 Mautic to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Mautic
Early access
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