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FinanceDataArchitecture
Blank (Crédit Agricole)

When your database handles the load but blocks your decisions

Before

0 dashboards

to manage growth: teams operate without data

After

1 FTE

saved on back-office processing

1 FTE

saved on back-office processing

Real-time

extraction of operational data

Self-service

BI opened up to non-IT teams

Blank, the Crédit Agricole group neobank dedicated to freelancers, offers a fully digital service: a business bank account, automated VAT, management of URSSAF social contributions. Growth is there, but the back-office and product teams are working without visibility. The DynamoDB architecture, designed for low-latency operational scalability, exposes no native analytics surface. The result: zero reliable dashboards, time-consuming manual exports, and decisions made on intuition.

THE STORY

Blank is growing. The Crédit Agricole group neobank for freelancers (business account, automated VAT, built-in URSSAF (French social security) contributions) onboards new customers every week. But internally, the teams are operating in the dark: no reliable dashboards, CSV exports done by hand, decisions made on instinct.

The problem is not a lack of data. It is an architecture designed to do something else.

The DynamoDB dead end: the core database is an operational success: low latency, scalability, availability. But DynamoDB is not built for analytical aggregations. A table scan in production means latency injected into the customers’ critical path. Non-IT teams cannot write ad hoc queries. Every analysis request goes up to the developers, and slows everyone down.

The solution: separate the planes. Nobori Partners designs a decoupled architecture: DynamoDB Streams captures every write in real time without touching the source table. Lambda functions transform and route the events to dedicated analytical storage. Metabase exposes a SQL interface accessible to operations staff, with no ticket and no waiting.

From POC to production in 4 months: the solution is fully declared as Infrastructure as Code (CloudFormation). Reproducible, versionable, auditable. Knowledge transfer is built into the engagement: the internal teams take over from go-live.

The result: 1 FTE freed up on back-office processing, business teams self-sufficient with their data, and an analytics architecture that evolves on its own, without ever interfering with production.

OUR APPROACH

4 steps to get there

1

Audit of the existing architecture

A complete analysis of the system: DynamoDB data model, access patterns, application data flows, code repository. The verdict is clear: the operational architecture is optimized for transactional performance, not for ad hoc analytical queries. Costly aggregations and table scans are out of reach without degrading production.

AWS DynamoDB
2

Identifying the target solution

Exploration of the extraction options: batch export, native Change Data Capture, replication. DynamoDB Streams stands out: it captures every change in real time without affecting the source table. Paired with Lambda for transformation and routing, it opens the way to a lightweight analytical data lake, separate from the operational plane.

DynamoDB StreamsLambda
3

Design and POC

End-to-end solution architecture: DynamoDB Streams → transformation Lambda → analytical storage → Metabase. The POC validates data consistency, extraction latency (near real time) and the Metabase user experience for non-technical users. Business teams can write their first SQL queries without IT support.

AWS LambdaServerless FrameworkMetabase
4

Industrialization and production rollout

From POC to production through Infrastructure as Code (CloudFormation). Every component (streams, Lambda functions, IAM permissions, analytical database) is declared, versioned and reproducible. Deployment assistance and knowledge transfer to the internal teams.

CloudFormationMetabaseServerless Framework

RESULTS

The measurable impact

1 FTEsaved on back-office processing
Real-timeextraction of operational data
Self-serviceBI opened up to non-IT teams

Real-time extraction pipeline in production in 4 months. Back-office and product teams access their data on their own through Metabase, with no IT ticket and no manual export. 1 FTE saved on back-office processing, streamlined operational processes, and a decoupled analytics architecture that evolves independently of the transactional plane.

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