Apache Airflow is the industry-standard workflow orchestrator with 10+ years of maturity and massive community adoption, while Dagster is a newer asset-oriented platform (founded 2018) offering superior data lineage tracking and type safety. Airflow excels in scale and ecosystem, while Dagster provides better developer experience for complex data pipelines.
Apache Airflow vs Dagster — Side-by-side comparison on A Versus B
Open-source workflow orchestration platform for data pipelines using task-based DAGs
Python-first data orchestration platform for complex workflow and asset management
First Release Date. GitHub Stars. Orchestration Paradigm. Data Lineage Tracking. Type Checking. Community Operators/Integrations. Enterprise Support
Choose Apache Airflow if you need battle-tested, production-proven orchestration at massive scale, have complex multi-cloud deployments, or require extensive third-party integrations. Choose Dagster if you prioritize data quality, asset management, developer productivity, and modern Python-first development with strong type safety and lineage tracking.
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: 2,000+ operators | Dagster: 50+
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: 9 years (2015) | Dagster: 5 years (2019)
Apache Airflow: External tools required | Dagster: Native assertions & sensors
Apache Airflow: Minimal type hints, runtime validation | Dagster: Full type hints, static validation
Apache Airflow: Task-centric DAGs | Dagster: Asset-centric with lineage tracking
Estimated Learning Curve (Hours to Productivity)
Apache Airflow: 20-30 hours | Dagster: 40-60 hours
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: Manual configuration required | Dagster: Automatic and built-in
Apache Airflow: Limited (runtime only) | Dagster: Strong (compile-time + runtime)
Apache Airflow: 15,000+ | Dagster: 2,500+
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) | Dagster: N/A
Setup Complexity (Configuration Files Required)
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 | Dagster: N/A
Enterprise Adoption (Fortune 500 Users Reported)
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 | Dagster: N/A
Community Repository Stars (as of Feb 2025)
Apache Airflow: 35,800 GitHub stars | Dagster: N/A
Apache Airflow: 50,000+ | Dagster: ~12,000 stars
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: 36,000+ | Dagster: 8,500+
Apache Airflow: Task-level only (limited) | Dagster: Full asset-level lineage
Apache Airflow: Minimal (manual validation) | Dagster: First-class type definitions
Apache Airflow: 6-8 weeks to proficiency | Dagster: 3-4 weeks to proficiency
Apache Airflow: Astronomer Cloud (third-party) | Dagster: Dagster Cloud (official)
Apache Airflow: 2M+ (proven in production) | Dagster: 100K+ (typical deployments)
Apache Airflow: N/A | Dagster: Python 3.8
Apache Airflow: N/A | Dagster: 45-60 minutes
Apache Airflow: N/A | 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+
Time to First Working Pipeline (typical)
Apache Airflow: N/A | 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: 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: 5 languages (Python, Rust, Golang, SQL, Bash)
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: 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
How to choose between Apache Airflow and Dagster
Apache Airflow vs Dagster — Structured Comparison Data
Machine-readable comparison dataset for Apache Airflow vs Dagster. Contains 131 attribute comparisons, 5 FAQ pairs.
Apache Airflow vs Dagster — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Airflow vs Dagster.
Which is easier to learn for new developers?
Dagster has a gentler learning curve (3-4 weeks average) due to its asset-oriented model that directly maps to business concepts. Airflow requires more infrastructure knowledge and typically takes 6-8 weeks to reach proficiency because it emphasizes task scheduling, operator selection, and DAG composition. Dagster's comprehensive documentation and modern Python API also reduce setup friction.
Which handles larger-scale deployments better?
Apache Airflow scales to massively larger workloads—production deployments regularly execute 2M+ tasks daily at companies like Netflix, Spotify, and Uber. Dagster is production-ready but typically handles 100K-500K daily tasks in mature deployments. For ultra-large scale, Airflow's 10+ year optimization history and proven infrastructure patterns give it the edge.
Which provides better data lineage and governance?
Dagster is purpose-built for data lineage with native asset-level tracking showing exactly which assets depend on others and how data flows through transformations. Airflow's lineage is limited to task dependencies and requires external tools (OpenLineage, data catalogs) to achieve similar visibility. For data governance and regulatory compliance, Dagster requires fewer workarounds.
Can I migrate from Airflow to Dagster or vice versa?
Migration is possible but non-trivial. Airflow DAGs (task graphs) must be restructured as Dagster asset definitions, requiring 2-4 weeks per 100 tasks. The paradigm shift from tasks to assets means refactoring logic, not just converting syntax. Reverse migration is equally complex. Plan for significant engineering effort and phased migration strategies.
Which is better for analytics/data engineering teams?
Dagster is optimized for analytics engineering workflows with its asset-driven model, built-in data quality expectations, and software-engineering-first approach. Airflow is more general-purpose and requires custom solutions for asset tracking. If your primary use case is transforming data and tracking assets (dbt, SQL, Python), Dagster provides faster time-to-value. If you need orchestration for diverse workloads (ML training, ETL, reporting), Airflow is more flexible.
How to Choose Between Apache Airflow and Dagster
First Release Date: Apache Airflow has 2014, while Dagster has 2018.
GitHub Stars: Apache Airflow has 36,000+, while Dagster has 8,500+.
Orchestration Paradigm: Apache Airflow has Task-based DAGs, while Dagster has Asset-oriented graphs.
Data Lineage Tracking: Apache Airflow has Limited (task-level only), while Dagster has Native asset-level lineage.
Type Checking: Apache Airflow has Minimal built-in support, while Dagster has First-class type definitions.
Consider: Community Operators/Integrations
Community Operators/Integrations: Apache Airflow has 300+ built-in providers, while Dagster has 50+ integrations.
Enterprise Support: Apache Airflow has Astronomer (third-party SaaS), while Dagster has Dagster Cloud (official).