Mambu to DuckDB Integration

How Datrise loads Mambu into DuckDB

Datrise syncs Mambu's clients, loan accounts, deposit accounts, transactions, repayments, and products into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Mambu: Cloud core banking and lending engine.

DuckDB: In-process analytics database for fast local OLAP.

How Mambu entities map to DuckDB

Mambu entity	DuckDB object	Notes
clients	`mambu_clients`	id PK · custom fields → JSON or STRUCT 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 DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

How does the Mambu to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

Connect Mambu to DuckDB the easy way

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