DatriseAI-first ETL

Moskit CRM to Databricks SQL Warehouse Integration

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

How Datrise loads Moskit CRM into Databricks SQL Warehouse

Datrise syncs Moskit CRM's contacts, companies, deals, funnel stages, tasks, and sales goals 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

Moskit CRM: Sales CRM for Brazilian inside-sales teams.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Moskit CRM entities map to Databricks SQL Warehouse

Moskit CRM entityDatabricks SQL Warehouse objectNotes
contactsmoskit_contactsid PK · custom fields → VARIANT or STRUCT columns
companiesmoskit_companiesid PK · linked to moskit_contacts
dealsmoskit_dealsid PK · linked to moskit_contacts
funnel stagesmoskit_funnel_stagesid PK · linked to moskit_contacts

FAQ

How does Datrise handle Moskit CRM'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 Moskit CRM 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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