DatriseAI-first ETL

Braintree Databricks SQL Warehouse

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

How Datrise loads Braintree into Databricks SQL Warehouse

Datrise syncs Braintree's transactions, customers, disputes, and settlement records 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

Braintree: PayPal-owned payment gateway for card and wallet transactions.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Braintree entities map to Databricks SQL Warehouse

Braintree entityDatabricks SQL Warehouse objectNotes
transactionsbraintree_transactionsid PK · custom fields → VARIANT or STRUCT columns
customersbraintree_customersid PK · linked to braintree_transactions
disputesbraintree_disputesid PK · linked to braintree_transactions
settlement recordsbraintree_settlement_recordsid PK · linked to braintree_transactions

FAQ

How does Datrise handle Braintree'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 Braintree 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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