Apache Airflow vs Apache Spark: Key Differences & When to Use Each

Comprehensive side-by-side comparison of features, pricing, and metrics

Key Differences

Compare Apache Airflow and Apache Spark across features, pricing, integrations, and community metrics. Apache Airflow / Apache Spark.

Feature

Apache Airflow

Workflow

Apache Spark

Data Processing

Side-by-side comparison of developer tools
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
✓ Free
Enterprise: Contact sales
✓ Free
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
94/100 (stable)
79/100 (stable)
Community Health
91/100 (excellent)
91/100 (excellent)
Maturity Index
88/100 (mature)
90/100 (mature)
Innovation Score
88/100 (pioneering)
91/100 (pioneering)
Risk Score (higher is safer)
94/100 (minimal)
94/100 (minimal)
Developer Experience
80/100 (good)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Apache Airflow Strengths

  • ✓ More popular (45,276 stars)
  • ✓ Larger community (4,323 contributors)

Apache Spark Strengths

When to Use Apache Airflow vs Apache Spark

Use Apache Airflow when its strengths align better with your stack and team needs, and choose Apache Spark when its ecosystem, integrations, or cost profile is a better fit.

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Data source: GitHub API

Last updated: 5/4/2026