DatriseAI-first ETL

3PL Central to Databricks SQL Warehouse Integration

AI-first ETL from 3PL Central into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads 3PL Central into Databricks SQL Warehouse

Datrise syncs 3PL Central's orders, receipts, inventory levels, items, customers, and warehouses 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

3PL Central: Third-party logistics warehouse and order operations.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How 3PL Central entities map to Databricks SQL Warehouse

3PL Central entityDatabricks SQL Warehouse objectNotes
orderstplcentral_ordersid PK · custom fields → VARIANT or STRUCT columns
receiptstplcentral_receiptsid PK · linked to tplcentral_orders
inventory levelstplcentral_inventory_levelsid PK · linked to tplcentral_orders
itemstplcentral_itemsid PK · linked to tplcentral_orders

FAQ

How does Datrise handle 3PL Central'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 3PL Central 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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