[ Tech stack ]
Dagster
The asset-oriented data orchestrator for declarative, testable and observable pipelines.
Dagster is an open-source orchestrator that thinks in assets rather than tasks: every table, file or model is declared in Python as a software-defined asset, and the dependency graph, lineage and freshness follow from it. Partitions and backfills replay a date range, asset checks validate quality on every materialization, and dbt, Spark, Snowflake or Airbyte integrations plug into the same graph. It's our choice when a data team wants to test pipelines locally and know, asset by asset, what is up to date.
[ Why Dagster at Dexon ]
What this technology does well, and why we use it.
Typical usage: ELT and dbt orchestration, ML feature pipelines, internal data platforms, migration from Airflow.
- 01
Software-defined assets: lineage, dependencies and freshness derived from code.
- 02
Partitions and backfills: replay a date range in one click.
- 03
Asset checks and declarative automation: rule-driven quality and refreshes.
- 04
Testable local development, native dbt integration, managed Dagster+ with branch deployments.
[ 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.
Nationwide coverage by BFM Business, Le Figaro, Challenges, La Tribune and CNews. An outside reading of our work and our innovations.
