Airflow is a mature, widely-adopted workflow orchestrator with 10+ years of community support and broader ecosystem integration, while Dagster is a modern asset-oriented orchestrator launched in 2019 that prioritizes data lineage, testability, and developer experience with stronger type safety and cleaner dependency definitions.
Dagster vs Apache Airflow — Side-by-side comparison on A Versus B
Modern asset-oriented data orchestration platform with first-class data lineage and testability.
Mature, widely-adopted workflow orchestration platform using DAG-based task scheduling and dependency management.
Initial Release Year. Primary Paradigm. GitHub Stars. Built-in Type Safety. Data Lineage Tracking. Job Testing Framework. Production Deployments (reported)
Choose Apache Airflow if you need mature, battle-tested orchestration with the largest community, extensive third-party integrations, and established enterprise support—it's ideal for teams with existing Airflow expertise and complex legacy pipelines. Choose Dagster if you prioritize modern Python development practices, native data asset management, built-in testing capabilities, and cleaner dependency graphs—it's better suited for new data platforms and teams building from scratch with strong type safety requirements.
Dagster: Python 3.8 | Apache Airflow: N/A
Dagster: 45-60 minutes | Apache Airflow: N/A
Dagster: Automatic and built-in | Apache Airflow: Manual configuration required
Dagster: Yes - Dagster asset checks | Apache Airflow: N/A
Dagster: 12+ (K8s, Docker, ECS, serverless) | Apache Airflow: N/A
Dagster: Free for self-hosted, $99/month for Dagster+ | Apache Airflow: N/A
Time to First Working Pipeline (typical)
Dagster: 4-6 hours | Apache Airflow: N/A
Dagster: Enterprise-grade built-in | Apache Airflow: N/A
Dagster: 6+ (K8s, Docker, serverless, hybrid) | Apache Airflow: N/A
Dagster: 20-30 minutes | Apache Airflow: N/A
Dagster: ~200 lines | Apache Airflow: N/A
Dagster: Native asset-level (100%) | Apache Airflow: N/A
Dagster: 100% of features | Apache Airflow: N/A
Dagster: 2 CPU / 4GB RAM minimum | Apache Airflow: N/A
Dagster: ~15+ features (full compliance suite) | Apache Airflow: N/A
Dagster: ~9.2k stars | Apache Airflow: N/A
Dagster: 50+ | Apache Airflow: 2,000+ operators
Dagster: Built-in Dagster Types with validation | Apache Airflow: N/A
Dagster: Yes, native support | Apache Airflow: N/A
Dagster: ~50 connectors | Apache Airflow: N/A
Dagster: 8-24 hours | Apache Airflow: N/A
Dagster: Enterprise-grade (dynamic, conditional) | Apache Airflow: N/A
Dagster: Advanced (Python/software engineering) | Apache Airflow: N/A
Dagster: 9/10 (advanced custom logic) | Apache Airflow: N/A
Dagster: Usage-based (compute units) | Apache Airflow: N/A
Dagster: Native, built-in with asset graph | Apache Airflow: N/A
Dagster: Yes - native DAG, scheduling, dynamic branching | Apache Airflow: N/A
Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week) | Apache Airflow: N/A
Dagster: End-to-end pipeline orchestration and execution | Apache Airflow: N/A
Dagster: Cloud-agnostic; works with 15+ systems | Apache Airflow: N/A
Dagster: 40-60 hours (requires Python learning) | Apache Airflow: N/A
Dagster: 9,800+ stars | Apache Airflow: N/A
Dagster: 2,000+ (estimated from public case studies) | Apache Airflow: N/A
Dagster: 9,200+ | Apache Airflow: 35,000+
Dagster: 5 years (2019) | Apache Airflow: 9 years (2015)
Dagster: Native assertions & sensors | Apache Airflow: External tools required
Dagster: Asset-centric with lineage tracking | Apache Airflow: Task-centric DAGs
Estimated Learning Curve (Hours to Productivity)
Dagster: 40-60 hours | Apache Airflow: 20-30 hours
Dagster: Strong (compile-time + runtime) | Apache Airflow: Limited (runtime only)
Dagster: 2,500+ | Apache Airflow: 15,000+
Dagster: Full asset-level lineage | Apache Airflow: Task-level only (limited)
Dagster: First-class type definitions | Apache Airflow: Minimal (manual validation)
Dagster: 3-4 weeks to proficiency | Apache Airflow: 6-8 weeks to proficiency
Dagster: Dagster Cloud (official) | Apache Airflow: Astronomer Cloud (third-party)
Dagster: 100K+ (typical deployments) | Apache Airflow: 2M+ (proven in production)
Dagster: 50+ connectors | Apache Airflow: N/A
Dagster: 7/10 (Python required) | Apache Airflow: N/A
Dagster: 24-48 hours (development needed) | Apache Airflow: N/A
Dagster: Full native integration | Apache Airflow: N/A
Dagster: 5-10 days | Apache Airflow: N/A
Dagster: 9,200+ stars | Apache Airflow: N/A
Dagster: 100+ (Python, Spark, K8s, APIs, databases, cloud) | Apache Airflow: N/A
Dagster: 40-80 hours (requires Python, orchestration concepts) | Apache Airflow: N/A
Dagster: Yes - built-in with Dagster Daemon | Apache Airflow: N/A
Dagster: Yes - built-in monitoring, alerting, and error tracking | Apache Airflow: N/A
Dagster: Python, SQL, Spark, Go, shell scripts, APIs | Apache Airflow: N/A
Dagster: 15-25 hours (setup + learning) | Apache Airflow: N/A
Dagster: Not applicable | Apache Airflow: N/A
Dagster: 40-90 minutes | Apache Airflow: N/A
Dagster: Manual tracking via assets; no built-in staging workflow | Apache Airflow: N/A
Dagster: Native asset graphs with automatic lineage, impact analysis, and upstream/downstream visibility | Apache Airflow: N/A
Dagster: 9,500+ | Apache Airflow: 35,200
Dagster: Framework-agnostic via Python ops; requires custom integration | Apache Airflow: N/A
Dagster: Python 3.8+ | Apache Airflow: N/A
Maximum Concurrent Runs (single instance)
Dagster: 500+ (optimized for high-volume asset materialization) | Apache Airflow: N/A
Dagster: Built-in Python type hints with automatic validation and schema enforcement | Apache Airflow: Minimal type hints, runtime validation
Dagster: 2,000+ | Apache Airflow: 10,000+
Dagster: Limited (community growing) | Apache Airflow: 800+
Dagster: Yes (first-class asset lineage) | Apache Airflow: No (requires manual/external tooling)
Dagster: Yes (comprehensive in-process testing) | Apache Airflow: Limited (integration-focused)
Dagster: Strong (enforced type hints) | Apache Airflow: Optional (not enforced)
Dagster: Moderate-to-High (asset-oriented paradigm) | Apache Airflow: Moderate (DAG-based, familiar to many)
Dagster: N/A | Apache Airflow: 8-16 hours
Dagster: N/A | Apache Airflow: 10+ via adapters
Dagster: N/A | Apache Airflow: Intermediate to Advanced
Dagster: N/A | Apache Airflow: E, L, T, testing, ML, monitoring (full stack)
Dagster: N/A | Apache Airflow: High (scheduler, web server, worker, database required)
Dagster: N/A | Apache Airflow: 120 minutes
Dagster: N/A | Apache Airflow: 50-70 lines
Dagster: N/A | Apache Airflow: 4+ (scheduler, worker, DB, broker)
Dagster: N/A | Apache Airflow: 35,000+ stars
Dagster: N/A | Apache Airflow: Self-hosted (varies)
Dagster: N/A | Apache Airflow: Community or third-party paid
Dagster: N/A | Apache Airflow: Complex (DAG operators)
Dagster: N/A | Apache Airflow: 9+ years (since 2015)
Dagster: N/A | Apache Airflow: Days (practical limit)
Dagster: N/A | Apache Airflow: Python (primary), Java/Go/C# (limited)
Dagster: N/A | Apache Airflow: 1,200+ official operators
Dagster: N/A | Apache Airflow: Manual retry + task checkpointing
Dagster: N/A | Apache Airflow: 5+ (scheduler, webserver, DB, executor, metadata)
Dagster: N/A | Apache Airflow: Yes (Astronomer, cloud providers)
Dagster: N/A | Apache Airflow: 1,800+ Providers
Dagster: N/A | Apache Airflow: 5-10 days
Dagster: N/A | Apache Airflow: Yes (comprehensive)
Dagster: N/A | Apache Airflow: 500+ commits
Dagster: N/A | Apache Airflow: Yes (full support)
Dagster: N/A | Apache Airflow: Yes (PostgreSQL/MySQL)
Dagster: N/A | Apache Airflow: 10,000-3,600,000 ms (10 seconds to 1 hour typical)
Dagster: N/A | Apache Airflow: ~1,000-5,000 tasks/min
Dagster: N/A | Apache Airflow: 5-15 minutes (quick setup)
Dagster: N/A | Apache Airflow: Fully native (DAG definitions in pure Python)
Dagster: N/A | Apache Airflow: At-least-once (with retries)
Dagster: N/A | Apache Airflow: Advanced (DAG viewer, logs, metrics, triggers)
Dagster: N/A | Apache Airflow: Java 8+ (optional; Python primary)
Dagster: N/A | Apache Airflow: Requires external components (Celery/Kubernetes/RabbitMQ)
Dagster: N/A | Apache Airflow: 8-12+ files (scheduler, executor, database, webserver configs)
Dagster: N/A | Apache Airflow: Community-driven (paid support via third parties)
Dagster: N/A | Apache Airflow: 250-400 MB
Dagster: N/A | Apache Airflow: 30-45 minutes
Dagster: N/A | Apache Airflow: Yes (comprehensive web UI included)
Dagster: N/A | Apache Airflow: Native DAG-based automatic resolution
Dagster: N/A | Apache Airflow: 3 (PostgreSQL, MySQL, SQLite)
Setup Complexity (Configuration Files Required)
Dagster: N/A | Apache Airflow: 5-7 (airflow.cfg, DAG files, connections, secrets, logging config)
Dagster: N/A | Apache Airflow: 45-90 minutes with PostgreSQL + webserver setup
Dagster: N/A | Apache Airflow: 15+ core features (DAG visualization, execution history, logs, task duration, SLAs, alerts, backfill)
Dagster: N/A | Apache Airflow: 200+ officially maintained operators + community operators
Enterprise Adoption (Fortune 500 Users Reported)
Dagster: N/A | Apache Airflow: Airbnb, Amazon, Google, Netflix, Twitter (estimated 80+ F500)
Dagster: N/A | Apache Airflow: 1 (PostgreSQL backend only, no message queue required)
Dagster: N/A | Apache Airflow: 500-800 MB baseline
Community Repository Stars (as of Feb 2025)
Dagster: N/A | Apache Airflow: 35,800 GitHub stars
Dagster: N/A | Apache Airflow: PostgreSQL/MySQL required
Dagster: N/A | Apache Airflow: 4-8 hours
Dagster: N/A | Apache Airflow: Manual configuration
Dagster: N/A | Apache Airflow: No (third-party only)
Dagster: N/A | Apache Airflow: 2.10+ (December 2024)
Dagster: N/A | Apache Airflow: 45-60 (with Kubernetes/Celery)
Dagster: N/A | Apache Airflow: 32+ (configurable)
Dagster: N/A | Apache Airflow: Required (PostgreSQL/MySQL/SQLite)
Dagster: N/A | Apache Airflow: 20-30 minutes
Dagster: N/A | Apache Airflow: Yes (comprehensive dashboard included)
Dagster: N/A | Apache Airflow: Recommended <5000 (performance degrades with larger DAGs)
Dagster: N/A | Apache Airflow: Primary: PostgreSQL, MySQL (uses database as queue)
Dagster: N/A | Apache Airflow: 36,200 stars
Dagster: N/A | Apache Airflow: Advanced (exponential backoff, custom retry policies, SLA alerts)
Dagster: N/A | Apache Airflow: Enterprise standard for data pipelines (Uber, Netflix, Airbnb, Google)
How to choose between Dagster and Apache Airflow
Dagster vs Apache Airflow — Structured Comparison Data
Machine-readable comparison dataset for Dagster vs Apache Airflow. Contains 164 attribute comparisons, 5 FAQ pairs.
Dagster vs Apache Airflow — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Dagster vs Apache Airflow.
Which is easier to learn for someone new to orchestration?
Apache Airflow has a gentler on-ramp due to its ubiquity, extensive tutorials, and simpler mental model (DAGs are intuitive). However, Airflow's flexibility can lead to inconsistent patterns. Dagster has steeper initial learning but cleaner abstractions once understood. Teams with Python experience may find Dagster's asset-oriented approach more natural long-term.
Dagster's asset-based architecture natively tracks data lineage automatically, showing which assets depend on which and enabling impact analysis. Airflow requires manual implementation via task naming conventions or external tools like OpenLineage. This is a key architectural difference favoring Dagster for data-heavy organizations.
Migration is possible but not automatic. Dagster provides conversion patterns and documentation for DAGs to assets, but significant refactoring is required. Many organizations run both systems in parallel during transition. Airflow→Dagster is easier than reverse migration due to Airflow's maturity.
Which is better for testing data pipelines?
Dagster is significantly better for testing. It includes built-in op testing, resource mocking, and in-process execution without external infrastructure. Airflow testing typically requires integration test setup, external frameworks, or DAG serialization tests. Dagster's approach is more aligned with modern software testing practices.
Which has better enterprise support and vendor options?
Airflow has more vendor options: Astronomer (founded 2017, dedicated provider), Google Cloud Composer, AWS MWAA, and others. Dagster is primarily supported by Dagster Labs (company founded 2020). Airflow's ecosystem is more mature, but Dagster Labs is rapidly growing. Both offer paid support tiers.
How to Choose Between Dagster and Apache Airflow
Initial Release Year: Dagster has 2019, while Apache Airflow has 2014.
Primary Paradigm: Dagster has Asset-oriented (focuses on data assets), while Apache Airflow has Task-oriented (focuses on DAGs/workflows).
GitHub Stars: Dagster has 9,200+, while Apache Airflow has 35,000+.
Built-in Type Safety: Dagster has Strong (Python type hints enforced), while Apache Airflow has Minimal (optional typing).
Data Lineage Tracking: Dagster has Native/first-class (asset lineage built-in), while Apache Airflow has Manual/third-party required.
Job Testing Framework: Dagster has Comprehensive in-process testing, while Apache Airflow has Limited, requires external setup.
Consider: Production Deployments (reported)
Production Deployments (reported): Dagster has 2,000+ organizations, while Apache Airflow has 10,000+ organizations.