DatriseAI-first ETL

Mautic Databricks SQL Warehouse

AI-first ETL from Mautic into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Mautic into Databricks SQL Warehouse

Datrise syncs Mautic's contacts, accounts, deals, activities, and lifecycle events into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Mautic: Open-source CRM for customizable sales and customer workflows.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Mautic entities map to Databricks SQL Warehouse

Mautic entityDatabricks SQL Warehouse objectNotes
contactsmautic_contactsid PK · custom fields → VARIANT or STRUCT columns
accountsmautic_accountsid PK · linked to mautic_contacts
dealsmautic_dealsid PK · linked to mautic_contacts
activitiesmautic_activitiesTIMESTAMP events

FAQ

How does Datrise handle Mautic's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Mautic to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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