Mambu to Spotfire Integration
AI-first ETL from Mambu into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mambu into Spotfire
Datrise syncs Mambu's clients, loan accounts, deposit accounts, transactions, repayments, and products into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
Mambu: Cloud core banking and lending engine.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How Mambu entities map to Spotfire
| Mambu entity | Spotfire object | Notes |
|---|---|---|
| clients | mambu_clients | id PK · custom fields → flattened columns for visualizations |
| loan accounts | mambu_loan_accounts | id PK · linked to mambu_clients |
| deposit accounts | mambu_deposit_accounts | id PK · linked to mambu_clients |
| transactions | mambu_transactions | id PK · linked to mambu_clients |
FAQ
How does Datrise handle Mambu's custom fields in Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the Mambu to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
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Early access
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