DatriseAI-first ETL

NetSuite Databricks SQL Warehouse

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

How Datrise loads NetSuite into Databricks SQL Warehouse

Datrise syncs NetSuite's transactions, customers, items, subsidiaries, and GL 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

NetSuite: Cloud ERP for finance, inventory, and operations.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How NetSuite entities map to Databricks SQL Warehouse

NetSuite entityDatabricks SQL Warehouse objectNotes
transactionsnetsuite_transactionsid PK · custom fields → VARIANT or STRUCT columns
customersnetsuite_customersid PK · linked to netsuite_transactions
itemsnetsuite_itemsid PK · linked to netsuite_transactions
subsidiariesnetsuite_subsidiariesid PK · linked to netsuite_transactions

FAQ

How does Datrise handle NetSuite'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 NetSuite 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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