[ 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 building block to see its scope.
[ Reassurance ]
- 0+
- custom projects delivered
- 30
- engineers, designers, project managers
- 80 %
- from top French schools
- 24 h
- average reply time
[ They trust us ]
More than 100 French and European companies trust us
[ Press ]
They talk about us.
Application and data division.
Nationwide coverage by BFM Business, Le Figaro, Challenges, La Tribune and CNews. An outside reading of our work and our innovations.
