DatriseAI-first ETL

Invoiced to Databricks SQL Warehouse Integration

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

How Datrise loads Invoiced into Databricks SQL Warehouse

Datrise syncs Invoiced's invoices, customers, payments, subscriptions, credit notes, and line items 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

Invoiced: Accounts receivable automation and billing.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Invoiced entities map to Databricks SQL Warehouse

Invoiced entityDatabricks SQL Warehouse objectNotes
invoicesinvoiced_invoicesid PK · custom fields → VARIANT or STRUCT columns
customersinvoiced_customersid PK · linked to invoiced_invoices
paymentsinvoiced_paymentsid PK · linked to invoiced_invoices
subscriptionsinvoiced_subscriptionsid PK · linked to invoiced_invoices

FAQ

How does Datrise handle Invoiced'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 Invoiced 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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