Workflow Orchestrationopen-sourceTrending
Dagster
Cloud-native data pipeline orchestrator with asset-based programming model
Visit websiteTechnical Profile
Scalability
high
Performance
high
Learning Curve
moderate
Maturity
stable
Languages: Python
Architecture: asset-based, type-safe, declarative
When to Use
- +Asset-centric pipelines
- +Data quality focus
- +Modern data stack
- +Testing important
When Not to Use
- -Simple cron jobs
- -Legacy Airflow heavy
- -Non-Python teams
Strengths
- Asset-based model
- Type safety
- Excellent testing
- 11k+ stars
- Modern DX
- Built-in data quality
Weaknesses
- Different paradigm from Airflow
- Smaller community
- Cloud features paywalled
Operations
Maintenance
moderate
Monitoring
low
Backup/Recovery
moderate
Hosting: self-hosted, cloud, managed
Quick Facts
- Category
- Workflow Orchestration
- License
- open source
- Pricing
- freemium (free tier)
- Community
- large
- Docs Quality
- excellent
- Trend
- rapidly growing
- Vendor Lock-in
- low
- Data Portability
- easy
Compliance
GDPR
HIPAA
SOC 2
PCI-DSS
Encryption
Audit Logs
RBAC
MFA
Best For
startupsmallmediumlarge
Use Cases
- Data pipelines
- Asset management
- ML pipelines
- Analytics engineering
- Data platform
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