Mambu to Mode Integration
AI-first ETL from Mambu into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mambu into Mode
Datrise syncs Mambu's clients, loan accounts, deposit accounts, transactions, repayments, and products into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Mambu: Cloud core banking and lending engine.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Mambu entities map to Mode
| Mambu entity | Mode object | Notes |
|---|---|---|
| clients | mambu_clients | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Mambu to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Mambu
Early access
Connect Mambu to Mode the easy way
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