DatriseAI-first ETL

kvCORE to Databricks SQL Warehouse Integration

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

How Datrise loads kvCORE into Databricks SQL Warehouse

Datrise syncs kvCORE's leads, contacts, listings, smart campaigns, property alerts, and agent activity 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

kvCORE: Real estate platform for lead generation and agent websites.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How kvCORE entities map to Databricks SQL Warehouse

kvCORE entityDatabricks SQL Warehouse objectNotes
leadskvcore_leadsid PK · custom fields → VARIANT or STRUCT columns
contactskvcore_contactsid PK · linked to kvcore_leads
listingskvcore_listingsid PK · linked to kvcore_leads
smart campaignskvcore_smart_campaignsid PK · linked to kvcore_leads

FAQ

How does Datrise handle kvCORE'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 kvCORE 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

Early access

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