DatriseAI-first ETL

Vincle to Databricks SQL Warehouse Integration

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

How Datrise loads Vincle into Databricks SQL Warehouse

Datrise syncs Vincle's customers, visits, orders, product assortments, promotions, and sales routes 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

Vincle: Field sales and trade execution for consumer goods.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Vincle entities map to Databricks SQL Warehouse

Vincle entityDatabricks SQL Warehouse objectNotes
customersvincle_customersid PK · custom fields → VARIANT or STRUCT columns
visitsvincle_visitsid PK · linked to vincle_customers
ordersvincle_ordersid PK · linked to vincle_customers
product assortmentsvincle_product_assortmentsid PK · linked to vincle_customers

FAQ

How does Datrise handle Vincle'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 Vincle 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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