Data & Analytics

Data engineering

Robust batch and streaming pipelines, ingestion frameworks and transformation layers with testing and lineage.

Vue d’ensemble

Data engineering is where most analytics and AI programs succeed or fail. If pipelines are fragile, slow or poorly documented, every report and model built on them inherits the problem. We build pipelines that are reliable, observable and easy to change.

Our engineers design ingestion from databases, SaaS applications, files, APIs and event streams, using metadata-driven frameworks so new sources can be added quickly and consistently. Transformations are written as version-controlled code with automated tests, documentation and lineage, following layered modeling patterns that keep raw, cleansed and business-ready data clearly separated.

For real-time needs, we implement streaming pipelines and change data capture where the business case justifies them. Every pipeline ships with monitoring, alerting and clear ownership, and CI/CD moves changes to production safely. The result is data that arrives on time, can be traced back to its source and can be trusted by the people who use it.

Ce que vous obtenez

01Ingestion framework covering priority source systems
02Tested, documented transformation layer
03Batch and streaming pipelines with orchestration
04Data quality checks, monitoring and alerting
05CI/CD for data code and deployment runbooks

Technologies que nous maîtrisons

Apache Airflow
dbt
Apache Spark
Apache Kafka
Fivetran
Azure Data Factory
AWS Glue
Debezium

Nous sommes indépendants des éditeurs : les outils sont choisis en fonction de vos besoins et de vos investissements existants.

Les questions que l’on nous pose souvent

Do we need real-time streaming?

Only where decisions genuinely depend on fresh data, such as fraud detection, operational monitoring or personalization. Many use cases are well served by frequent batch loads, which are simpler and cheaper to run.

Can you work with our existing pipelines?

Yes. We often stabilize and refactor existing pipelines before extending them, starting with the ones that feed the most important reports and models.