Apache Airflow vs Apache Spark: Differences cles et quand utiliser chaque outil
Analyse approfondie des différences clés, cas d'usage et signaux de communauté.
Differences cles
Comparez Apache Airflow et Apache Spark selon les fonctionnalites, le prix, les integrations et les metriques de communaute. Apache Airflow / Apache Spark.
Fonctionnalité
Apache Airflow
Workflow
Apache Spark
Data Processing
Comparez des outils développeur côte à côte avec des métriques réelles.
Platform to programmatically author and schedule workflows
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 45 276
⭐ 43 233
Contributors
👥 4 323
👥 3 432
Pricing
✓ Gratuit
Enterprise: Contact sales
✓ Gratuit
Enterprise: Contact sales
Languages
Python
Scala
Features
- • Airflow
- • Apache
- • Apache Airflow
- • Automation
- • Dag
- • Big Data
- • Java
- • Jdbc
- • Python
- • R
Integrations
No integrations listed
No integrations listed
Momentum Score
94/100Momentum949494
(stable)
79/100Momentum797979
(stable)
Community Health
91/100Health919191
(excellent)
91/100Health919191
(excellent)
Maturity Index
88/100Maturity888888
(mature)
90/100Maturity909090
(mature)
Innovation Score
88/100Innovation888888
(pioneering)
91/100Innovation919191
(pioneering)
Risk Score (higher is safer)
94/100Risk949494
(minimal)
94/100Risk949494
(minimal)
Developer Experience
80/100DX808080
(good)
80/100DX808080
(good)
Links
Apache Airflow Strengths
- ✓ More popular (45 276 étoiles)
- ✓ Larger community (4 323 contributors)
Apache Spark Strengths
Quand utiliser Apache Airflow vs Apache Spark
Utilisez Apache Airflow quand ses points forts correspondent mieux a votre stack et a votre equipe, et choisissez Apache Spark si son ecosysteme, ses integrations ou son cout sont plus adaptes.
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Data source: GitHub API
Dernière mise à jour 5/4/2026