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

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

Key Differences

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

Feature

Dagster

Workflow

Apache Spark

Data Processing

Side-by-side comparison of developer tools
Data orchestrator for machine learning and analytics
Unified analytics engine for large-scale data processing
GitHub Stars
⭐ 15,427
⭐ 43,233
Contributors
👥 673
👥 3,432
Pricing
✓ Free
Enterprise: Contact sales
✓ Free
Enterprise: Contact sales
Languages
Python
Scala
Features
  • • Analytics
  • • Dagster
  • • Data Engineering
  • • Data Integration
  • • Data Orchestrator
  • • Big Data
  • • Java
  • • Jdbc
  • • Python
  • • R
Integrations
No integrations listed
No integrations listed
Momentum Score
94/100 (stable)
79/100 (stable)
Community Health
50/100 (developing)
91/100 (excellent)
Maturity Index
45/100 (emerging)
90/100 (mature)
Innovation Score
43/100 (evolving)
91/100 (pioneering)
Risk Score (higher is safer)
53/100 (low)
94/100 (minimal)
Developer Experience
54/100 (needs-improvement)
80/100 (good)
Links
🌐 Visit Website View on GitHub
🌐 Visit Website View on GitHub

Dagster Strengths

Apache Spark Strengths

  • ✓ More popular (43,233 stars)
  • ✓ Larger community (3,432 contributors)

When to Use Dagster vs Apache Spark

Use Dagster 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.

Related comparisons

Dagster vs Apache Airflow → Apache Spark vs Apache Flink → Dagster vs Prefect → Apache Spark vs Apache Beam → Dagster vs n8n → Apache Spark vs Elasticsearch → Dagster vs Elasticsearch → Apache Spark vs Redis → Dagster vs Redis → Apache Spark vs Grafana → Dagster vs Grafana → Apache Spark vs Prometheus →

More Comparisons

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Data source: GitHub API

Last updated: 5/4/2026