Your data, structured for action.

Data Engineering transforms data that is scattered and difficult to exploit into reliable, structured flows. It often constitutes the foundation required for reporting, Business Intelligence, automation and artificial intelligence projects.

Duplicated data, manual reporting, contradictory figures, fragile pipelines or difficulty launching AI projects are generally good indicators. An audit makes it possible to determine what really needs to be kept, corrected or rebuilt.

Yes. We can ingest data from APIs, SQL/NoSQL databases, ERP, CRM, files, SaaS platforms or event streams. The aim is to centralise the useful information without unnecessarily modifying the existing business tools.

The Data Warehouse is particularly suited to structured data and decision-support analysis. The Data Lake accepts more varied and raw data, while the Lakehouse seeks to combine the flexibility of the Lake with the management guarantees of the Warehouse.

In an ETL, data is transformed before it is loaded into the target system. In an ELT, it is loaded first and then transformed directly within the Data platform. The choice depends on the architecture, the volumes and the uses.

Yes. We can audit the existing pipelines, data models, databases and tools, and then modernise only the components that require it. A gradual migration is often preferable when a system is already being used on a daily basis.

We can put in place validation rules, automated tests, schema checks, anomaly detection, pipeline monitoring and alerts. Traceability also makes it possible to identify where a piece of data comes from and the transformations it has undergone.

The choice depends on your existing IT system, internal skills, the services already in use, security constraints and future Data/AI uses. We favour the environment offering the best compromise rather than an imposed provider.

The duration depends on the number of sources, the volumes, the quality of the data and the level of transformation expected. A first architecture can be put in place gradually, then extended business domain by business domain rather than launching a global migration in a single step.

Not in every case. Some projects can directly use data that is already exploitable. When data is scattered, unreliable or difficult to access, however, structuring the Data foundation can be necessary before hoping to industrialise AI properly.