DatriseAI-first ETL

Nectar CRM to Databricks SQL Warehouse Integration

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

How Datrise loads Nectar CRM into Databricks SQL Warehouse

Datrise syncs Nectar CRM's contacts, companies, opportunities, sales stages, activities, and quotes 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

Nectar CRM: B2B sales CRM with playbooks for Brazilian teams.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Nectar CRM entities map to Databricks SQL Warehouse

Nectar CRM entityDatabricks SQL Warehouse objectNotes
contactsnectar_crm_contactsid PK · custom fields → VARIANT or STRUCT columns
companiesnectar_crm_companiesid PK · linked to nectar_crm_contacts
opportunitiesnectar_crm_opportunitiesid PK · linked to nectar_crm_contacts
sales stagesnectar_crm_sales_stagesid PK · linked to nectar_crm_contacts

FAQ

How does Datrise handle Nectar 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 Nectar 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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