Mautic → DuckDB
AI-first ETL from Mautic into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mautic into DuckDB
Datrise syncs Mautic's contacts, accounts, deals, activities, and lifecycle events into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.
Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.
Ideal for local and notebook analytics without standing up a server.
Endpoints
Mautic: Open-source CRM for customizable sales and customer workflows.
DuckDB: In-process analytics database for fast local OLAP.
How Mautic entities map to DuckDB
| Mautic entity | DuckDB object | Notes |
|---|---|---|
| contacts | mautic_contacts | id PK · custom fields → JSON or STRUCT columns |
| accounts | mautic_accounts | id PK · linked to mautic_contacts |
| deals | mautic_deals | id PK · linked to mautic_contacts |
| activities | mautic_activities | TIMESTAMP WITH TIME ZONE events |
FAQ
How does Datrise handle Mautic's custom fields in DuckDB?
Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.
How does the Mautic to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
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Early access
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