DatriseAI-first ETL

GoCardless to Databricks SQL Warehouse Integration

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

How Datrise loads GoCardless into Databricks SQL Warehouse

Datrise syncs GoCardless's payments, customers, mandates, subscriptions, payouts, and refunds 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

GoCardless: Bank debit collection and recurring payments.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How GoCardless entities map to Databricks SQL Warehouse

GoCardless entityDatabricks SQL Warehouse objectNotes
paymentsgocardless_paymentsid PK · custom fields → VARIANT or STRUCT columns
customersgocardless_customersid PK · linked to gocardless_payments
mandatesgocardless_mandatesid PK · linked to gocardless_payments
subscriptionsgocardless_subscriptionsid PK · linked to gocardless_payments

FAQ

How does Datrise handle GoCardless'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 GoCardless 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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