Apache Airflow vs Dagster
type: Article
description: 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.
language: en-US
date published: 2026-07-08T08:35:57.908Z
date created: 2026-07-08T08:35:57.908Z
date modified: 2026-07-08T08:35:58.597Z
content reference time: 2026-07-08T08:35:58.597Z
author: A Versus B
publisher: A Versus B
about: Apache Airflow
about: Dagster
abstract: 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.
text: First Release Date. GitHub Stars. Orchestration Paradigm. Data Lineage Tracking. Type Checking. Community Operators/Integrations. Enterprise Support
conclusion text: 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.
additional property: Monthly PyPI/Package Downloads (2024)
  type: PropertyValue
  value: Apache Airflow: 2.8M | Dagster: N/A
additional property: Time to First Pipeline (expert user)
  type: PropertyValue
  value: Apache Airflow: 8-16 hours | Dagster: N/A
additional property: Native Data Warehouse Support
  type: PropertyValue
  value: Apache Airflow: 10+ via adapters | Dagster: N/A
additional property: Minimum Python Knowledge Required
  type: PropertyValue
  value: Apache Airflow: Intermediate to Advanced | Dagster: N/A
additional property: Open Source Contributors
  type: PropertyValue
  value: Apache Airflow: 1,200+ | Dagster: N/A
additional property: Core Use Case Scope
  type: PropertyValue
  value: Apache Airflow: E, L, T, testing, ML, monitoring (full stack) | Dagster: N/A
additional property: Infrastructure Setup Complexity
  type: PropertyValue
  value: Apache Airflow: High (scheduler, web server, worker, database required) | Dagster: N/A
additional property: Time to Production (First Workflow)
  type: PropertyValue
  value: Apache Airflow: 120 minutes | Dagster: N/A
additional property: Lines of Code (Basic ETL Task)
  type: PropertyValue
  value: Apache Airflow: 50-70 lines | Dagster: N/A
additional property: Available Integrations
  type: PropertyValue
  value: Apache Airflow: 2,000+ operators | Dagster: 50+
  value reference: Apache Airflow
additional property: Minimum Infrastructure Requirements
  type: PropertyValue
  value: Apache Airflow: 4+ (scheduler, worker, DB, broker) | Dagster: N/A
additional property: GitHub Stars (Community Indicator)
  type: PropertyValue
  value: Apache Airflow: 35,000+ stars | Dagster: N/A
additional property: Uptime SLA (Managed Services)
  type: PropertyValue
  value: Apache Airflow: Self-hosted (varies) | Dagster: N/A
additional property: Enterprise Support Availability
  type: PropertyValue
  value: Apache Airflow: Community or third-party paid | Dagster: N/A
additional property: Configuration as Code Simplicity
  type: PropertyValue
  value: Apache Airflow: Complex (DAG operators) | Dagster: N/A
additional property: GitHub Stars (Community Maturity)
  type: PropertyValue
  value: Apache Airflow: 22,000+ | Dagster: N/A
additional property: Project Age
  type: PropertyValue
  value: Apache Airflow: 9+ years (since 2015) | Dagster: N/A
additional property: Maximum Workflow Duration
  type: PropertyValue
  value: Apache Airflow: Days (practical limit) | Dagster: N/A
additional property: Supported Programming Languages (SDKs)
  type: PropertyValue
  value: Apache Airflow: Python (primary), Java/Go/C# (limited) | Dagster: N/A
additional property: Pre-built Integrations/Operators
  type: PropertyValue
  value: Apache Airflow: 1,200+ official operators | Dagster: N/A
additional property: Fault Tolerance Method
  type: PropertyValue
  value: Apache Airflow: Manual retry + task checkpointing | Dagster: N/A
additional property: Minimum Deployment Complexity
  type: PropertyValue
  value: Apache Airflow: 5+ (scheduler, webserver, DB, executor, metadata) | Dagster: N/A
additional property: Enterprise Commercial Support Available
  type: PropertyValue
  value: Apache Airflow: Yes (Astronomer, cloud providers) | Dagster: N/A
additional property: Time Since First Release
  type: PropertyValue
  value: Apache Airflow: 9 years (2015) | Dagster: 5 years (2019)
  value reference: Apache Airflow
additional property: Pre-built Integrations
  type: PropertyValue
  value: Apache Airflow: 1,000+ | Dagster: 150+
  value reference: Apache Airflow
additional property: Built-in Data Quality Testing
  type: PropertyValue
  value: Apache Airflow: External tools required | Dagster: Native assertions & sensors
additional property: Type Safety & Validation
  type: PropertyValue
  value: Apache Airflow: Minimal type hints, runtime validation | Dagster: Full type hints, static validation
additional property: Data Lineage Model
  type: PropertyValue
  value: Apache Airflow: Task-centric DAGs | Dagster: Asset-centric with lineage tracking
additional property: Estimated Learning Curve (Hours to Productivity)
  type: PropertyValue
  value: Apache Airflow: 20-30 hours | Dagster: 40-60 hours
  value reference: Apache Airflow
additional property: Active Contributors (Monthly)
  type: PropertyValue
  value: Apache Airflow: 150+ | Dagster: 40+
  value reference: Apache Airflow
additional property: Native Integrations
  type: PropertyValue
  value: Apache Airflow: 1,800+ Providers | Dagster: N/A
additional property: Time to First Productive Workflow
  type: PropertyValue
  value: Apache Airflow: 5-10 days | Dagster: N/A
additional property: Minimum RAM Requirement
  type: PropertyValue
  value: Apache Airflow: 1-2 GB | Dagster: N/A
additional property: Built-in Web Dashboard
  type: PropertyValue
  value: Apache Airflow: Yes (comprehensive) | Dagster: N/A
additional property: Annual Commit Activity
  type: PropertyValue
  value: Apache Airflow: 500+ commits | Dagster: N/A
additional property: Dynamic DAG Support
  type: PropertyValue
  value: Apache Airflow: Yes (full support) | Dagster: N/A
additional property: External Database Required
  type: PropertyValue
  value: Apache Airflow: Yes (PostgreSQL/MySQL) | Dagster: N/A
additional property: Processing Latency
  type: PropertyValue
  value: Apache Airflow: 10,000-3,600,000 ms (10 seconds to 1 hour typical) | Dagster: N/A
additional property: Maximum Throughput per Node
  type: PropertyValue
  value: Apache Airflow: ~1,000-5,000 tasks/min | Dagster: N/A
additional property: Time to Deploy Pipeline
  type: PropertyValue
  value: Apache Airflow: 5-15 minutes (quick setup) | Dagster: N/A
additional property: Python Support Level
  type: PropertyValue
  value: Apache Airflow: Fully native (DAG definitions in pure Python) | Dagster: N/A
additional property: State Consistency Guarantee
  type: PropertyValue
  value: Apache Airflow: At-least-once (with retries) | Dagster: N/A
additional property: Integrated Web UI
  type: PropertyValue
  value: Apache Airflow: Advanced (DAG viewer, logs, metrics, triggers) | Dagster: N/A
additional property: Minimum Java Version Required
  type: PropertyValue
  value: Apache Airflow: Java 8+ (optional; Python primary) | Dagster: N/A
additional property: Initial Release
  type: PropertyValue
  value: Apache Airflow: 2014 | Dagster: N/A
additional property: Market Share Adoption
  type: PropertyValue
  value: Apache Airflow: 68% | Dagster: N/A
additional property: Available Providers/Integrations
  type: PropertyValue
  value: Apache Airflow: 300+ | Dagster: N/A
additional property: Time to Proficiency
  type: PropertyValue
  value: Apache Airflow: 40-80 | Dagster: N/A
additional property: Cloud-Native Architecture
  type: PropertyValue
  value: Apache Airflow: Requires external components (Celery/Kubernetes/RabbitMQ) | Dagster: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-airflow
significant link: https://www.aversusb.net/alternatives/apache-airflow
significant link: https://www.aversusb.net/entity/dagster
significant link: https://www.aversusb.net/alternatives/dagster
significant link: https://www.aversusb.net/api/answer/airflow-vs-dagster)
significant link: https://www.aversusb.net/api/v1/schema/airflow-vs-dagster)
related link: https://www.aversusb.net/compare/dagster-vs-airflow
related link: https://www.aversusb.net/compare/airflow-vs-dagster
related link: https://www.aversusb.net/compare/dagster-vs-metaflow
related link: https://www.aversusb.net/compare/mlflow-vs-dagster
related link: https://www.aversusb.net/compare/prefect-vs-airflow
related link: https://www.aversusb.net/compare/dagster-vs-mage
related link: https://www.aversusb.net/compare/airflow-vs-dbt
related link: https://www.aversusb.net/compare/kubeflow-vs-airflow
teaches: How to choose between Apache Airflow and Dagster
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Apache Airflow vs Dagster — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Apache Airflow
type: SoftwareApplication
alternate name: AA
language: en-US
description: Open-source workflow orchestration platform for data pipelines using task-based DAGs
image: ImageObject
  caption: Apache Airflow
subject of: Apache Airflow vs Dagster
image: ImageObject
  caption: Apache Airflow

Dagster
type: SoftwareApplication
language: en-US
description: Python-first data orchestration platform for complex workflow and asset management
subject of: Apache Airflow vs Dagster

Apache Airflow vs Dagster — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Apache Airflow vs Dagster. Contains 131 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
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distribution: Structured FAQ pairs JSON
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  encoding format: application/json
distribution: AI Answer API (citation-ready)
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  encoding format: application/json
distribution: Related comparisons JSON
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  encoding format: application/json
variable measured: Monthly PyPI/Package Downloads (2024)
  type: PropertyValue
  value reference: Adoption
variable measured: Time to First Pipeline (expert user)
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  value reference: Ease of Use
variable measured: Native Data Warehouse Support
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variable measured: Minimum Python Knowledge Required
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variable measured: Open Source Contributors
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variable measured: Core Use Case Scope
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variable measured: Infrastructure Setup Complexity
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variable measured: Lines of Code (Basic ETL Task)
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variable measured: Available Integrations
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  value reference: Ecosystem
variable measured: Minimum Infrastructure Requirements
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variable measured: GitHub Stars (Community Indicator)
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variable measured: Uptime SLA (Managed Services)
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variable measured: Enterprise Support Availability
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variable measured: Configuration as Code Simplicity
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variable measured: GitHub Stars (Community Maturity)
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variable measured: Project Age
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variable measured: Maximum Workflow Duration
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variable measured: Supported Programming Languages (SDKs)
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variable measured: Pre-built Integrations/Operators
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variable measured: Type Safety & Validation
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variable measured: Data Lineage Model
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variable measured: Estimated Learning Curve (Hours to Productivity)
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variable measured: Active Contributors (Monthly)
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variable measured: Built-in Web Dashboard
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variable measured: Annual Commit Activity
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variable measured: External Database Required
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variable measured: Processing Latency
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variable measured: Maximum Throughput per Node
  type: PropertyValue
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variable measured: Time to Deploy Pipeline
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variable measured: Python Support Level
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variable measured: State Consistency Guarantee
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variable measured: Integrated Web UI
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variable measured: Minimum Java Version Required
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variable measured: Initial Release
  type: PropertyValue
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variable measured: Market Share Adoption
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variable measured: Available Providers/Integrations
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variable measured: Time to Proficiency
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variable measured: Cloud-Native Architecture
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variable measured: Minimum Setup Complexity
  type: PropertyValue
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variable measured: Enterprise Support Plans
  type: PropertyValue
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variable measured: First Release Year
  type: PropertyValue
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variable measured: Production Deployments (estimated)
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variable measured: Built-in Data Lineage
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variable measured: Provider/Integration Count
  type: PropertyValue
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variable measured: Type Safety Support
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variable measured: Minimum Python Version
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variable measured: Community Slack Members
  type: PropertyValue
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variable measured: Memory Usage at Idle
  type: PropertyValue
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variable measured: Setup Time for Hello World
  type: PropertyValue
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variable measured: Built-in UI/Dashboard
variable measured: Task Dependency Management
variable measured: Supported Message Brokers
variable measured: Setup Complexity (Configuration Files Required)
variable measured: Time to Deploy First Task (Minutes)
variable measured: Web UI Completeness
variable measured: Supported Task Types / Operators
variable measured: Enterprise Adoption (Fortune 500 Users Reported)
variable measured: Default Message Broker Options
is based on: https://www.aversusb.net/compare/airflow-vs-dagster)

Apache Airflow vs Dagster — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Apache Airflow vs Dagster.
has defined term: Adoption
has defined term: Ease of Use
has defined term: Integration
has defined term: Accessibility
has defined term: Community
has defined term: Functionality
has defined term: Operations
has defined term: Deployment & Setup
has defined term: Development
has defined term: Ecosystem
has defined term: Infrastructure
has defined term: Reliability
has defined term: Support
has defined term: Adoption & Community
has defined term: Maturity
has defined term: Capability
has defined term: Language Support
has defined term: Extensibility
has defined term: Features
has defined term: Developer Experience
has defined term: Architecture
has defined term: Onboarding
has defined term: Community Activity
has defined term: Integrations
has defined term: Development Activity
has defined term: Features & Ecosystem
has defined term: System Requirements
has defined term: Performance
has defined term: Data Reliability
has defined term: Monitoring
has defined term: Requirements
has defined term: Usability
has defined term: Setup
has defined term: Community Support
has defined term: Flexibility
has defined term: Deployment
has defined term: Installation
has defined term: Market
has defined term: Data Governance
has defined term: Performance & Scale
has defined term: Compatibility
has defined term: Pricing
has defined term: Community Adoption
has defined term: Cost
has defined term: Learning Resources
has defined term: Enterprise
has defined term: Code Quality
has defined term: Production Readiness
has defined term: Implementation Speed
has defined term: Transformation
has defined term: Cost Structure
has defined term: Core Function
has defined term: Learning
has defined term: Speed

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Accessibility
type: DefinedTerm
term code: accessibility
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Deployment & Setup
type: DefinedTerm
term code: deployment-&-setup
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Development
type: DefinedTerm
term code: development
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Adoption & Community
type: DefinedTerm
term code: adoption-&-community
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Maturity
type: DefinedTerm
term code: maturity
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Language Support
type: DefinedTerm
term code: language-support
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Extensibility
type: DefinedTerm
term code: extensibility
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Onboarding
type: DefinedTerm
term code: onboarding
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Community Activity
type: DefinedTerm
term code: community-activity
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Development Activity
type: DefinedTerm
term code: development-activity
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Features & Ecosystem
type: DefinedTerm
term code: features-&-ecosystem
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

System Requirements
type: DefinedTerm
term code: system-requirements
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Data Reliability
type: DefinedTerm
term code: data-reliability
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Monitoring
type: DefinedTerm
term code: monitoring
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Requirements
type: DefinedTerm
term code: requirements
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Community Support
type: DefinedTerm
term code: community-support
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Installation
type: DefinedTerm
term code: installation
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Market
type: DefinedTerm
term code: market
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Data Governance
type: DefinedTerm
term code: data-governance
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Performance & Scale
type: DefinedTerm
term code: performance-&-scale
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Learning Resources
type: DefinedTerm
term code: learning-resources
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Code Quality
type: DefinedTerm
term code: code-quality
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Production Readiness
type: DefinedTerm
term code: production-readiness
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Implementation Speed
type: DefinedTerm
term code: implementation-speed
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Transformation
type: DefinedTerm
term code: transformation
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Cost Structure
type: DefinedTerm
term code: cost-structure
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Core Function
type: DefinedTerm
term code: core-function
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Learning
type: DefinedTerm
term code: learning
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

Speed
type: DefinedTerm
term code: speed
in defined term set: Apache Airflow vs Dagster — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-08T08:35:58.597Z
author: A Versus B
publisher: A Versus B
is part of: airflow-vs-dagster)
main entity: Which is easier to learn for new developers?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which handles larger-scale deployments better?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which provides better data lineage and governance?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can I migrate from Airflow to Dagster or vice versa?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which is better for analytics/data engineering teams?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between Apache Airflow and Dagster
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: First Release Date
step: Consider: GitHub Stars
step: Consider: Orchestration Paradigm
step: Consider: Data Lineage Tracking
step: Consider: Type Checking
step: Consider: Community Operators/Integrations
step: Consider: Enterprise Support
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: First Release Date
type: HowToStep
position: 1
text: First Release Date: Apache Airflow has 2014, while Dagster has 2018.

Consider: GitHub Stars
type: HowToStep
position: 2
text: GitHub Stars: Apache Airflow has 36,000+, while Dagster has 8,500+.

Consider: Orchestration Paradigm
type: HowToStep
position: 3
text: Orchestration Paradigm: Apache Airflow has Task-based DAGs, while Dagster has Asset-oriented graphs.

Consider: Data Lineage Tracking
type: HowToStep
position: 4
text: Data Lineage Tracking: Apache Airflow has Limited (task-level only), while Dagster has Native asset-level lineage.

Consider: Type Checking
type: HowToStep
position: 5
text: Type Checking: Apache Airflow has Minimal built-in support, while Dagster has First-class type definitions.

Consider: Community Operators/Integrations
type: HowToStep
position: 6
text: Community Operators/Integrations: Apache Airflow has 300+ built-in providers, while Dagster has 50+ integrations.

Consider: Enterprise Support
type: HowToStep
position: 7
text: Enterprise Support: Apache Airflow has Astronomer (third-party SaaS), while Dagster has Dagster Cloud (official).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Apache Airflow vs Dagster
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Apache Airflow vs Dagster

Minimum Setup Complexity
type: PropertyValue
value: Apache Airflow: 8-12+ files (scheduler, executor, database, webserver configs) | Dagster: N/A

Enterprise Support Plans
type: PropertyValue
value: Apache Airflow: Community-driven (paid support via third parties) | Dagster: N/A

First Release Year
type: PropertyValue
value: Apache Airflow: 2014 | Dagster: 2019
value reference: Apache Airflow

Production Deployments (estimated)
type: PropertyValue
value: Apache Airflow: 50,000+ | Dagster: 500+
value reference: Apache Airflow

Built-in Data Lineage
type: PropertyValue
value: Apache Airflow: Manual configuration required | Dagster: Automatic and built-in

Provider/Integration Count
type: PropertyValue
value: Apache Airflow: 350+ | Dagster: ~50
value reference: Apache Airflow

Type Safety Support
type: PropertyValue
value: Apache Airflow: Limited (runtime only) | Dagster: Strong (compile-time + runtime)

Minimum Python Version
type: PropertyValue
value: Apache Airflow: 3.8+ | Dagster: 3.9+

Community Slack Members
type: PropertyValue
value: Apache Airflow: 15,000+ | Dagster: 2,500+
value reference: Apache Airflow

Memory Usage at Idle
type: PropertyValue
value: Apache Airflow: 250-400 MB | Dagster: N/A

Setup Time for Hello World
type: PropertyValue
value: Apache Airflow: 30-45 minutes | Dagster: N/A

Built-in UI/Dashboard
type: PropertyValue
value: Apache Airflow: Yes (comprehensive web UI included) | Dagster: N/A

Task Dependency Management
type: PropertyValue
value: Apache Airflow: Native DAG-based automatic resolution | Dagster: N/A

Supported Message Brokers
type: PropertyValue
value: Apache Airflow: 3 (PostgreSQL, MySQL, SQLite) | Dagster: N/A

Setup Complexity (Configuration Files Required)
type: PropertyValue
value: Apache Airflow: 5-7 (airflow.cfg, DAG files, connections, secrets, logging config) | Dagster: N/A

Time to Deploy First Task (Minutes)
type: PropertyValue
value: Apache Airflow: 45-90 minutes with PostgreSQL + webserver setup | Dagster: N/A

Web UI Completeness
type: PropertyValue
value: Apache Airflow: 15+ core features (DAG visualization, execution history, logs, task duration, SLAs, alerts, backfill) | Dagster: N/A

Supported Task Types / Operators
type: PropertyValue
value: Apache Airflow: 200+ officially maintained operators + community operators | Dagster: N/A

Enterprise Adoption (Fortune 500 Users Reported)
type: PropertyValue
value: Apache Airflow: Airbnb, Amazon, Google, Netflix, Twitter (estimated 80+ F500) | Dagster: N/A

Default Message Broker Options
type: PropertyValue
value: Apache Airflow: 1 (PostgreSQL backend only, no message queue required) | Dagster: N/A

Minimum Memory Per Worker (MB)
type: PropertyValue
value: Apache Airflow: 500-800 MB baseline | Dagster: N/A

Community Repository Stars (as of Feb 2025)
type: PropertyValue
value: Apache Airflow: 35,800 GitHub stars | Dagster: N/A

GitHub Stars
type: PropertyValue
value: Apache Airflow: 50,000+ | Dagster: ~12,000 stars
value reference: Apache Airflow

Active Contributors
type: PropertyValue
value: Apache Airflow: 5,000+ | Dagster: N/A

Minimum Database Setup
type: PropertyValue
value: Apache Airflow: PostgreSQL/MySQL required | Dagster: N/A

Enterprise Production Adoption
type: PropertyValue
value: Apache Airflow: 72% | Dagster: N/A

Base Setup Time
type: PropertyValue
value: Apache Airflow: 4-8 hours | Dagster: N/A

Native Retry Logic
type: PropertyValue
value: Apache Airflow: Manual configuration | Dagster: N/A

Managed Cloud Option Available
type: PropertyValue
value: Apache Airflow: No (third-party only) | Dagster: N/A

GitHub Stars (Community Size)
type: PropertyValue
value: Apache Airflow: 36,000+ | Dagster: 8,500+
value reference: Apache Airflow

Built-in Provider Integrations
type: PropertyValue
value: Apache Airflow: 300+ | Dagster: 50+
value reference: Apache Airflow

First Official Release
type: PropertyValue
value: Apache Airflow: 2014 | Dagster: 2018
value reference: Apache Airflow

Native Asset Lineage Tracking
type: PropertyValue
value: Apache Airflow: Task-level only (limited) | Dagster: Full asset-level lineage

Type Safety Features
type: PropertyValue
value: Apache Airflow: Minimal (manual validation) | Dagster: First-class type definitions

Learning Curve Time (Average)
type: PropertyValue
value: Apache Airflow: 6-8 weeks to proficiency | Dagster: 3-4 weeks to proficiency
value reference: Dagster

Enterprise SaaS Option Available
type: PropertyValue
value: Apache Airflow: Astronomer Cloud (third-party) | Dagster: Dagster Cloud (official)

Maximum Daily Task Executions (Tested)
type: PropertyValue
value: Apache Airflow: 2M+ (proven in production) | Dagster: 100K+ (typical deployments)
value reference: Apache Airflow

Minimum Python Version Supported
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Python 3.8

Time to First Pipeline (learning curve)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 45-60 minutes

Native Data Quality Checks
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Yes - Dagster asset checks

Deployment Configurations Supported
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 12+ (K8s, Docker, ECS, serverless)

SaaS Pricing (base tier)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Free for self-hosted, $99/month for Dagster+

First Release Date
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 2019

GitHub Stars (as of 2026)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 8,400+

Time to First Working Pipeline (typical)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 4-6 hours

Minimum Infrastructure Cost (Monthly)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: $200-500

Multi-Tenancy Support
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Enterprise-grade built-in

Python Version Support
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 3.8+

Supported Deployment Platforms
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 6+ (K8s, Docker, serverless, hybrid)

Documentation Quality (Page Count)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 800+

Time to First Workflow
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 20-30 minutes

Minimum Code for Basic Workflow
type: PropertyValue
value: Apache Airflow: N/A | Dagster: ~200 lines

Asset Lineage Tracking Coverage
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Native asset-level (100%)

Self-Hosted Feature Parity
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 100% of features

Startup Overhead for Self-Hosted
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 2 CPU / 4GB RAM minimum

Enterprise Governance Features
type: PropertyValue
value: Apache Airflow: N/A | Dagster: ~15+ features (full compliance suite)

Community GitHub Stars
type: PropertyValue
value: Apache Airflow: N/A | Dagster: ~9.2k stars

Type Safety Feature
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Built-in Dagster Types with validation

Setup Time (Minutes)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 15-20

Automatic Lineage Detection
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Yes, native support

Managed Cloud SLA
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 99.9%

Pre-built Connectors
type: PropertyValue
value: Apache Airflow: N/A | Dagster: ~50 connectors

Minimum Time to First Data Pipeline
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 8-24 hours

Orchestration Complexity Support
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Enterprise-grade (dynamic, conditional)

Required Technical Skill Level
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Advanced (Python/software engineering)

Data Transformation Capabilities
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 9/10 (advanced custom logic)

Cloud Platform Pricing Model
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Usage-based (compute units)

Asset Lineage & Observability
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Native, built-in with asset graph

Built-in Orchestration Engine
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Yes - native DAG, scheduling, dynamic branching

Cloud Pricing (per compute unit/seat)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week)

Primary Use Case
type: PropertyValue
value: Apache Airflow: N/A | Dagster: End-to-end pipeline orchestration and execution

Supported Programming Languages
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 5 languages (Python, Rust, Golang, SQL, Bash)

Data Warehouse Integrations (Native)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Cloud-agnostic; works with 15+ systems

Time to Competency (for SQL analysts)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 40-60 hours (requires Python learning)

GitHub Community Stars
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 9,800+ stars

Enterprise Adoption (tracked companies)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 2,000+ (estimated from public case studies)

Pre-built Data Connectors
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 50+ connectors

Minimum Learning Curve (1-10 scale)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 7/10 (Python required)

Time to Deploy First Integration
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 24-48 hours (development needed)

Native dbt Integration
type: PropertyValue
value: Apache Airflow: N/A | Dagster: Full native integration

Typical Time to Build Custom Connector
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 5-10 days

Community Size (GitHub Stars)
type: PropertyValue
value: Apache Airflow: N/A | Dagster: 9,200+ stars

SpeakableSpecification
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Apache Airflow — Wikipedia
type: CreativeWork

Dagster — Wikipedia
type: CreativeWork

Compare Apache Airflow vs Dagster
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/airflow-vs-dagster)
object: Apache Airflow
object: Dagster

Built-in UI/Dashboard
type: PropertyValue
value reference: Monitoring

Task Dependency Management
type: PropertyValue
value reference: Features

Supported Message Brokers
type: PropertyValue
value reference: Flexibility

Setup Complexity (Configuration Files Required)
type: PropertyValue
value reference: Deployment

Time to Deploy First Task (Minutes)
type: PropertyValue
value reference: Deployment

Web UI Completeness
type: PropertyValue
value reference: Monitoring

Supported Task Types / Operators
type: PropertyValue
value reference: Functionality

Enterprise Adoption (Fortune 500 Users Reported)
type: PropertyValue
value reference: Adoption

Default Message Broker Options
type: PropertyValue
value reference: Architecture

Minimum Memory Per Worker (MB)
type: PropertyValue
value reference: Performance

Community Repository Stars (as of Feb 2025)
type: PropertyValue
value reference: Community

GitHub Stars
type: PropertyValue
value reference: Community

Active Contributors
type: PropertyValue
value reference: Community

Minimum Database Setup
type: PropertyValue
value reference: Installation

Enterprise Production Adoption
type: PropertyValue
value reference: Market

Base Setup Time
type: PropertyValue
value reference: Installation

Native Retry Logic
type: PropertyValue
value reference: Features

Managed Cloud Option Available
type: PropertyValue
value reference: Deployment

GitHub Stars (Community Size)
type: PropertyValue
value reference: Community

Built-in Provider Integrations
type: PropertyValue
value reference: Ecosystem

First Official Release
type: PropertyValue
value reference: Maturity

Native Asset Lineage Tracking
type: PropertyValue
value reference: Data Governance

Type Safety Features
type: PropertyValue
value reference: Developer Experience

Learning Curve Time (Average)
type: PropertyValue
value reference: Developer Experience

Enterprise SaaS Option Available
type: PropertyValue
value reference: Deployment

Maximum Daily Task Executions (Tested)
type: PropertyValue
value reference: Performance & Scale

Minimum Python Version Supported
type: PropertyValue
value reference: Compatibility

Time to First Pipeline (learning curve)
type: PropertyValue
value reference: Ease of Use

Native Data Quality Checks
type: PropertyValue
value reference: Data Governance

Deployment Configurations Supported
type: PropertyValue
value reference: Deployment

SaaS Pricing (base tier)
type: PropertyValue
value reference: Pricing

First Release Date
type: PropertyValue
value reference: Maturity

GitHub Stars (as of 2026)
type: PropertyValue
value reference: Community Adoption

Time to First Working Pipeline (typical)
type: PropertyValue
value reference: Developer Experience

Minimum Infrastructure Cost (Monthly)
type: PropertyValue
value reference: Cost

Multi-Tenancy Support
type: PropertyValue
value reference: Features

Python Version Support
type: PropertyValue
value reference: Compatibility

Supported Deployment Platforms
type: PropertyValue
value reference: Deployment

Documentation Quality (Page Count)
type: PropertyValue
value reference: Learning Resources

Time to First Workflow
type: PropertyValue
value reference: Developer Experience

Minimum Code for Basic Workflow
type: PropertyValue
value reference: Developer Experience

Asset Lineage Tracking Coverage
type: PropertyValue
value reference: Data Governance

Self-Hosted Feature Parity
type: PropertyValue
value reference: Deployment

Startup Overhead for Self-Hosted
type: PropertyValue
value reference: Infrastructure

Enterprise Governance Features
type: PropertyValue
value reference: Enterprise

Community GitHub Stars
type: PropertyValue
value reference: Adoption

Type Safety Feature
type: PropertyValue
value reference: Code Quality

Setup Time (Minutes)
type: PropertyValue
value reference: Ease of Use

Automatic Lineage Detection
type: PropertyValue
value reference: Features

Managed Cloud SLA
type: PropertyValue
value reference: Production Readiness

Pre-built Connectors
type: PropertyValue
value reference: Functionality

Minimum Time to First Data Pipeline
type: PropertyValue
value reference: Implementation Speed

Orchestration Complexity Support
type: PropertyValue
value reference: Features

Required Technical Skill Level
type: PropertyValue
value reference: Ease of Use

Data Transformation Capabilities
type: PropertyValue
value reference: Transformation

Cloud Platform Pricing Model
type: PropertyValue
value reference: Cost Structure

Asset Lineage & Observability
type: PropertyValue
value reference: Monitoring

Built-in Orchestration Engine
type: PropertyValue
value reference: Architecture

Cloud Pricing (per compute unit/seat)
type: PropertyValue
value reference: Cost

Primary Use Case
type: PropertyValue
value reference: Core Function

Supported Programming Languages
type: PropertyValue
value reference: Language Support

Data Warehouse Integrations (Native)
type: PropertyValue
value reference: Integration

Time to Competency (for SQL analysts)
type: PropertyValue
value reference: Learning

GitHub Community Stars
type: PropertyValue
value reference: Community

Enterprise Adoption (tracked companies)
type: PropertyValue
value reference: Adoption

Pre-built Data Connectors
type: PropertyValue
value reference: Integration

Minimum Learning Curve (1-10 scale)
type: PropertyValue
value reference: Usability

Time to Deploy First Integration
type: PropertyValue
value reference: Speed

Native dbt Integration
type: PropertyValue
value reference: Transformation

Typical Time to Build Custom Connector
type: PropertyValue
value reference: Development

Community Size (GitHub Stars)
type: PropertyValue
value reference: Community