Mautic → PostgreSQL
AI-first ETL from Mautic into PostgreSQL. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mautic into PostgreSQL
Datrise syncs Mautic's contacts, accounts, deals, activities, and lifecycle events into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.
Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.
Ideal for operational analytics and application backends that need fresh, queryable copies of your data.
Endpoints
Mautic: Open-source CRM for customizable sales and customer workflows.
PostgreSQL: Open-source relational database with strong SQL and extensions.
How Mautic entities map to PostgreSQL
| Mautic entity | PostgreSQL object | Notes |
|---|---|---|
| contacts | mautic_contacts | id PK · custom fields → jsonb columns |
| accounts | mautic_accounts | id PK · linked to mautic_contacts |
| deals | mautic_deals | id PK · linked to mautic_contacts |
| activities | mautic_activities | timestamptz events |
FAQ
How does Datrise handle Mautic's custom fields in PostgreSQL?
Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PostgreSQL types.
How does the Mautic to PostgreSQL sync stay up to date?
It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
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Early access
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