001 Architecture & Data Integration

The implementation of data pipelines, in real time as well as in batch, makes it possible to process and deliver information efficiently. These flows integrate data from multiple sources, whether business systems, IoT objects, APIs, ERP or CRM. Rigorous governance and complete traceability ensure the reliability and control of the entire data life cycle.

002 Data Platforms & Cloud

The design and deployment of Data Lakes and Data Warehouses, on solutions such as Snowflake, BigQuery, Redshift, or Synapse, make it possible to structure and value data at scale. These architectures are based on cloud-native environments such as AWS, Azure or GCP. Particular attention is paid to optimizing costs and performances in order to ensure the effectiveness of analytical platforms.

003 Streaming & Real Time

The ingestion and processing of massive data rely on technologies such as Apache Kafka, Pulsar, Flink, or Spark Streaming. These solutions allow the establishment of event-driven pipelines that promote decision-making in real time. The monitoring and resilience of production flows guarantee the continuity of service and the reliability of treatments.

004 Quality, Security & Governance

Cleaning, standardizing, and improving the quality of data is an essential step in ensuring that it is reliable. Environments are secured through encryption, masking, and rigorous access management (IAM). Finally, the implementation of catalogs and data governance policies, including the GDPR, data catalogs and lineage, makes it possible to structure, trace and protect data assets in a sustainable way.