Mambu to Snowflake Integration
AI-first ETL from Mambu into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mambu into Snowflake
Datrise syncs Mambu's clients, loan accounts, deposit accounts, transactions, repayments, and products into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.
Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.
Ideal for central analytics warehouses feeding BI and AI workloads.
Endpoints
Mambu: Cloud core banking and lending engine.
Snowflake: Cloud data warehouse with separated compute and storage.
How Mambu entities map to Snowflake
| Mambu entity | Snowflake object | Notes |
|---|---|---|
| clients | mambu_clients | id PK · custom fields → VARIANT columns |
| 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 Snowflake?
Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.
How does the Mambu to Snowflake sync stay up to date?
It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.
Related pipelines
More destinations for Mambu
More sources for Snowflake
Early access
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