[ Tech stack ]
Apache Airflow
The data-workflow orchestrator that became the industry standard.
Airflow describes pipelines as Python DAGs: tasks, dependencies, retries, scheduling. Its flexibility, provider ecosystem and clear UI make it the reference for orchestrating batch ETL, ML pipelines and analytical processing.
[ Why Apache Airflow at Dexon ]
What this technology does well,
and why we use it.
Typical usage: ETL orchestration, ML pipelines, analytical batch jobs.
- 01
DAGs in pure Python: readability, testability, versioning.
- 02
Native providers: AWS, GCP, Azure, Snowflake, dbt, Spark.
- 03
Cron-like scheduling, retries, SLA, alerting.
- 04
Astronomer, MWAA, Cloud Composer: managed on all three clouds.
[ Complementary technologies ]
The building blocks we often
mobilise alongside.
A stack rarely exists alone. Here are the technologies Dexon most often pairs with this one, through pipeline habits, usage similarity or internal mastery. Click on a brick to see its scope.
[ Reassurance ]
- 0+
- custom projects delivered
- 30
- engineers, designers, project managers
- 80 %
- from top French schools
- 24 h
- average reply time
[ Our AI stance ]
AI-augmented approach,
supervised by experts.
We use artificial intelligence as a lever for acceleration and optimisation within our technical processes, while keeping strong human oversight on every strategic phase of the project.
AI improves productivity. It does not replace:
- field experience
- architectural expertise
- understanding business stakes
- technical governance
- complex trade-offs
- cybersecurity
- operational accountability
Our teams act as a layer of validation, quality control, security hardening and steering, to ensure reliable, scalable deliverables that can operate in real-world environments.