— Technology we master

Apache Airflow

The data workflow orchestrator that became the industry standard.

Airflow describes pipelines in 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.

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Why Apache Airflow

What Apache Airflow brings to your project.

Typical use cases: ETL orchestration, ML pipelines, analytical batch jobs.

  1. 01

    DAGs in pure Python: readability, testability, versioning.

  2. 02

    Native providers: AWS, GCP, Azure, Snowflake, dbt, Spark.

  3. 03

    Cron-like scheduling, retries, SLA, alerting.

  4. 04

    Astronomer, MWAA, Cloud Composer: managed across three clouds.

Services

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Benefit from comprehensive, multidisciplinary expertise to support you through the design, development and maintenance of our applications.

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Complex algorithms and AI

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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

Beyond visual and technical design, as well as AI-augmented programming, 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.

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