DatriseAI-first ETL

Mambu to Databricks SQL Warehouse Integration

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

How Datrise loads Mambu into Databricks SQL Warehouse

Datrise syncs Mambu's clients, loan accounts, deposit accounts, transactions, repayments, and products 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

Mambu: Cloud core banking and lending engine.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Mambu entities map to Databricks SQL Warehouse

Mambu entityDatabricks SQL Warehouse objectNotes
clientsmambu_clientsid PK · custom fields → VARIANT or STRUCT columns
loan accountsmambu_loan_accountsid PK · linked to mambu_clients
deposit accountsmambu_deposit_accountsid PK · linked to mambu_clients
transactionsmambu_transactionsid PK · linked to mambu_clients

FAQ

How does Datrise handle Mambu'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 Mambu 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.

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