Mambu to Sisense Integration
AI-first ETL from Mambu into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mambu into Sisense
Datrise syncs Mambu's clients, loan accounts, deposit accounts, transactions, repayments, and products into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
Mambu: Cloud core banking and lending engine.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Mambu entities map to Sisense
| Mambu entity | Sisense object | Notes |
|---|---|---|
| clients | mambu_clients | id PK · custom fields → flattened columns for the cube |
| 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 Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the Mambu to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
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