Dagster vs Apache Airflow
type: Article
description: 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.
language: en-US
date published: 2026-07-09T18:45:14.954Z
date created: 2026-07-09T18:45:14.954Z
date modified: 2026-07-09T21:40:59.986Z
content reference time: 2026-07-09T21:40:59.986Z
author: A Versus B
publisher: A Versus B
about: Dagster
about: Apache Airflow
abstract: 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.
text: Initial Release Year. Primary Paradigm. GitHub Stars. Built-in Type Safety. Data Lineage Tracking. Job Testing Framework. Production Deployments (reported)
conclusion text: 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.
additional property: Minimum Python Version Supported
  type: PropertyValue
  value: Dagster: Python 3.8 | Apache Airflow: N/A
additional property: Time to First Pipeline (learning curve)
  type: PropertyValue
  value: Dagster: 45-60 minutes | Apache Airflow: N/A
additional property: Built-in Data Lineage
  type: PropertyValue
  value: Dagster: Automatic and built-in | Apache Airflow: Manual configuration required
additional property: Native Data Quality Checks
  type: PropertyValue
  value: Dagster: Yes - Dagster asset checks | Apache Airflow: N/A
additional property: Deployment Configurations Supported
  type: PropertyValue
  value: Dagster: 12+ (K8s, Docker, ECS, serverless) | Apache Airflow: N/A
additional property: SaaS Pricing (base tier)
  type: PropertyValue
  value: Dagster: Free for self-hosted, $99/month for Dagster+ | Apache Airflow: N/A
additional property: First Release Date
  type: PropertyValue
  value: Dagster: 2019 | Apache Airflow: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Dagster: 8,400+ | Apache Airflow: N/A
additional property: Time to First Working Pipeline (typical)
  type: PropertyValue
  value: Dagster: 4-6 hours | Apache Airflow: N/A
additional property: Minimum Infrastructure Cost (monthly)
  type: PropertyValue
  value: Dagster: $200-500 | Apache Airflow: N/A
additional property: Multi-Tenancy Support
  type: PropertyValue
  value: Dagster: Enterprise-grade built-in | Apache Airflow: N/A
additional property: Python Version Support
  type: PropertyValue
  value: Dagster: 3.8+ | Apache Airflow: N/A
additional property: Supported Deployment Platforms
  type: PropertyValue
  value: Dagster: 6+ (K8s, Docker, serverless, hybrid) | Apache Airflow: N/A
additional property: Documentation Quality (Page Count)
  type: PropertyValue
  value: Dagster: 800+ | Apache Airflow: N/A
additional property: Time to First Workflow
  type: PropertyValue
  value: Dagster: 20-30 minutes | Apache Airflow: N/A
additional property: Minimum Code for Basic Workflow
  type: PropertyValue
  value: Dagster: ~200 lines | Apache Airflow: N/A
additional property: Asset Lineage Tracking Coverage
  type: PropertyValue
  value: Dagster: Native asset-level (100%) | Apache Airflow: N/A
additional property: Self-Hosted Feature Parity
  type: PropertyValue
  value: Dagster: 100% of features | Apache Airflow: N/A
additional property: Startup Overhead for Self-Hosted
  type: PropertyValue
  value: Dagster: 2 CPU / 4GB RAM minimum | Apache Airflow: N/A
additional property: Enterprise Governance Features
  type: PropertyValue
  value: Dagster: ~15+ features (full compliance suite) | Apache Airflow: N/A
additional property: Community GitHub Stars
  type: PropertyValue
  value: Dagster: ~9.2k stars | Apache Airflow: N/A
additional property: First Release Year
  type: PropertyValue
  value: Dagster: 2019 | Apache Airflow: 2014
  value reference: Apache Airflow
additional property: Available Integrations
  type: PropertyValue
  value: Dagster: 50+ | Apache Airflow: 2,000+ operators
  value reference: Apache Airflow
additional property: Minimum Python Version
  type: PropertyValue
  value: Dagster: 3.9+ | Apache Airflow: 3.8+
additional property: Type Safety Feature
  type: PropertyValue
  value: Dagster: Built-in Dagster Types with validation | Apache Airflow: N/A
additional property: Setup Time (Minutes)
  type: PropertyValue
  value: Dagster: 15-20 | Apache Airflow: N/A
additional property: Automatic Lineage Detection
  type: PropertyValue
  value: Dagster: Yes, native support | Apache Airflow: N/A
additional property: Managed Cloud SLA
  type: PropertyValue
  value: Dagster: 99.9% | Apache Airflow: N/A
additional property: Pre-built Connectors
  type: PropertyValue
  value: Dagster: ~50 connectors | Apache Airflow: N/A
additional property: Minimum Time to First Data Pipeline
  type: PropertyValue
  value: Dagster: 8-24 hours | Apache Airflow: N/A
additional property: Orchestration Complexity Support
  type: PropertyValue
  value: Dagster: Enterprise-grade (dynamic, conditional) | Apache Airflow: N/A
additional property: Required Technical Skill Level
  type: PropertyValue
  value: Dagster: Advanced (Python/software engineering) | Apache Airflow: N/A
additional property: Data Transformation Capabilities
  type: PropertyValue
  value: Dagster: 9/10 (advanced custom logic) | Apache Airflow: N/A
additional property: Cloud Platform Pricing Model
  type: PropertyValue
  value: Dagster: Usage-based (compute units) | Apache Airflow: N/A
additional property: Asset Lineage & Observability
  type: PropertyValue
  value: Dagster: Native, built-in with asset graph | Apache Airflow: N/A
additional property: Built-in Orchestration Engine
  type: PropertyValue
  value: Dagster: Yes - native DAG, scheduling, dynamic branching | Apache Airflow: N/A
additional property: Cloud Pricing (per compute unit/seat)
  type: PropertyValue
  value: Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week) | Apache Airflow: N/A
additional property: Primary Use Case
  type: PropertyValue
  value: Dagster: End-to-end pipeline orchestration and execution | Apache Airflow: N/A
additional property: Data Warehouse Integrations (Native)
  type: PropertyValue
  value: Dagster: Cloud-agnostic; works with 15+ systems | Apache Airflow: N/A
additional property: Time to Competency (for SQL analysts)
  type: PropertyValue
  value: Dagster: 40-60 hours (requires Python learning) | Apache Airflow: N/A
additional property: GitHub Community Stars
  type: PropertyValue
  value: Dagster: 9,800+ stars | Apache Airflow: N/A
additional property: Enterprise Adoption (tracked companies)
  type: PropertyValue
  value: Dagster: 2,000+ (estimated from public case studies) | Apache Airflow: N/A
additional property: GitHub Stars (Community Size)
  type: PropertyValue
  value: Dagster: 9,200+ | Apache Airflow: 35,000+
  value reference: Apache Airflow
additional property: Time Since First Release
  type: PropertyValue
  value: Dagster: 5 years (2019) | Apache Airflow: 9 years (2015)
  value reference: Apache Airflow
additional property: Pre-Built Integrations
  type: PropertyValue
  value: Dagster: 150+ | Apache Airflow: 1,000+
  value reference: Apache Airflow
additional property: Built-in Data Quality Testing
  type: PropertyValue
  value: Dagster: Native assertions & sensors | Apache Airflow: External tools required
additional property: Data Lineage Model
  type: PropertyValue
  value: Dagster: Asset-centric with lineage tracking | Apache Airflow: Task-centric DAGs
additional property: Estimated Learning Curve (Hours to Productivity)
  type: PropertyValue
  value: Dagster: 40-60 hours | Apache Airflow: 20-30 hours
  value reference: Apache Airflow
additional property: Active Contributors (Monthly)
  type: PropertyValue
  value: Dagster: 40+ | Apache Airflow: 150+
  value reference: Apache Airflow
interactivity type: active
significant link: https://www.aversusb.net/entity/dagster
significant link: https://www.aversusb.net/alternatives/dagster
significant link: https://www.aversusb.net/entity/apache-airflow
significant link: https://www.aversusb.net/alternatives/apache-airflow
significant link: https://www.aversusb.net/api/answer/dagster-vs-airflow)
significant link: https://www.aversusb.net/api/v1/schema/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/dagster-vs-mage
related link: https://www.aversusb.net/compare/airflow-vs-dbt
related link: https://www.aversusb.net/compare/kubeflow-vs-airflow
related link: https://www.aversusb.net/compare/dagster-vs-prefect
related link: https://www.aversusb.net/compare/dagster-vs-airbyte
teaches: How to choose between Dagster and Apache Airflow
genre: Software

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

A Versus B
type: Organization

Dagster
type: SoftwareApplication
language: en-US
description: Modern asset-oriented data orchestration platform with first-class data lineage and testability.
subject of: Dagster vs Apache Airflow

Apache Airflow
type: SoftwareApplication
alternate name: AA
language: en-US
description: Mature, widely-adopted workflow orchestration platform using DAG-based task scheduling and dependency management.
image: ImageObject
  caption: Apache Airflow
subject of: Dagster vs Apache Airflow
image: ImageObject
  caption: Apache Airflow

Dagster vs Apache Airflow — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Dagster vs Apache Airflow. Contains 164 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
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Minimum Python Version Supported
  type: PropertyValue
  value reference: Compatibility
variable measured: Time to First Pipeline (learning curve)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Built-in Data Lineage
  type: PropertyValue
  value reference: Features
variable measured: Native Data Quality Checks
  type: PropertyValue
  value reference: Data Governance
variable measured: Deployment Configurations Supported
  type: PropertyValue
  value reference: Deployment
variable measured: SaaS Pricing (base tier)
  type: PropertyValue
  value reference: Pricing
variable measured: First Release Date
  type: PropertyValue
  value reference: Maturity
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community Adoption
variable measured: Time to First Working Pipeline (typical)
  type: PropertyValue
  value reference: Developer Experience
variable measured: Minimum Infrastructure Cost (monthly)
  type: PropertyValue
  value reference: Cost
variable measured: Multi-Tenancy Support
  type: PropertyValue
  value reference: Features
variable measured: Python Version Support
  type: PropertyValue
  value reference: Compatibility
variable measured: Supported Deployment Platforms
  type: PropertyValue
  value reference: Capability
variable measured: Documentation Quality (Page Count)
  type: PropertyValue
  value reference: Learning Resources
variable measured: Time to First Workflow
  type: PropertyValue
  value reference: Developer Experience
variable measured: Minimum Code for Basic Workflow
  type: PropertyValue
  value reference: Developer Experience
variable measured: Asset Lineage Tracking Coverage
  type: PropertyValue
  value reference: Data Governance
variable measured: Self-Hosted Feature Parity
  type: PropertyValue
  value reference: Deployment
variable measured: Startup Overhead for Self-Hosted
  type: PropertyValue
  value reference: Infrastructure
variable measured: Enterprise Governance Features
  type: PropertyValue
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variable measured: Community GitHub Stars
  type: PropertyValue
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variable measured: First Release Year
  type: PropertyValue
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variable measured: Available Integrations
  type: PropertyValue
  value reference: Ecosystem
variable measured: Minimum Python Version
  type: PropertyValue
  value reference: Requirements
variable measured: Type Safety Feature
  type: PropertyValue
  value reference: Code Quality
variable measured: Setup Time (Minutes)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Automatic Lineage Detection
  type: PropertyValue
  value reference: Features
variable measured: Managed Cloud SLA
  type: PropertyValue
  value reference: Production Readiness
variable measured: Pre-built Connectors
  type: PropertyValue
  value reference: Integration
variable measured: Minimum Time to First Data Pipeline
  type: PropertyValue
  value reference: Implementation Speed
variable measured: Orchestration Complexity Support
  type: PropertyValue
  value reference: Features
variable measured: Required Technical Skill Level
  type: PropertyValue
  value reference: Ease of Use
variable measured: Data Transformation Capabilities
  type: PropertyValue
  value reference: Transformation
variable measured: Cloud Platform Pricing Model
  type: PropertyValue
  value reference: Cost Structure
variable measured: Asset Lineage & Observability
  type: PropertyValue
  value reference: Monitoring
variable measured: Built-in Orchestration Engine
  type: PropertyValue
  value reference: Architecture
variable measured: Cloud Pricing (per compute unit/seat)
  type: PropertyValue
  value reference: Cost
variable measured: Primary Use Case
  type: PropertyValue
  value reference: Application Domain
variable measured: Data Warehouse Integrations (Native)
  type: PropertyValue
  value reference: Integration
variable measured: Time to Competency (for SQL analysts)
  type: PropertyValue
  value reference: Learning
variable measured: GitHub Community Stars
  type: PropertyValue
  value reference: Community
variable measured: Enterprise Adoption (tracked companies)
  type: PropertyValue
  value reference: Adoption
variable measured: GitHub Stars (Community Size)
  type: PropertyValue
  value reference: Community & Adoption
variable measured: Time Since First Release
  type: PropertyValue
  value reference: Maturity
variable measured: Pre-Built Integrations
  type: PropertyValue
  value reference: Features
variable measured: Built-in Data Quality Testing
  type: PropertyValue
  value reference: Features
variable measured: Data Lineage Model
  type: PropertyValue
  value reference: Architecture
variable measured: Estimated Learning Curve (Hours to Productivity)
  type: PropertyValue
  value reference: Onboarding
variable measured: Active Contributors (Monthly)
  type: PropertyValue
  value reference: Community Activity
variable measured: Production Deployments (Estimated)
  type: PropertyValue
  value reference: Adoption
variable measured: Provider/Integration Count
  type: PropertyValue
  value reference: Ecosystem
variable measured: Type Safety Support
  type: PropertyValue
  value reference: Developer Experience
variable measured: Community Slack Members
  type: PropertyValue
  value reference: Community Support
variable measured: Built-in Provider Integrations
  type: PropertyValue
  value reference: Ecosystem
variable measured: First Official Release
  type: PropertyValue
  value reference: Maturity
variable measured: Native Asset Lineage Tracking
  type: PropertyValue
  value reference: Data Governance
variable measured: Type Safety Features
  type: PropertyValue
  value reference: Developer Experience
variable measured: Learning Curve Time (Average)
  type: PropertyValue
  value reference: Developer Experience
variable measured: Enterprise SaaS Option Available
  type: PropertyValue
  value reference: Deployment
variable measured: Maximum Daily Task Executions (Tested)
  type: PropertyValue
  value reference: Performance & Scale
variable measured: Pre-built Data Connectors
variable measured: Minimum Learning Curve (1-10 scale)
variable measured: Time to Deploy First Integration
variable measured: Native dbt Integration
variable measured: Typical Time to Build Custom Connector
variable measured: Community Size (GitHub Stars)
variable measured: Number of Integrations
variable measured: Learning Curve (Developer Hours)
variable measured: Native Scheduling Support
is based on: https://www.aversusb.net/compare/dagster-vs-airflow)

Dagster vs Apache Airflow — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Dagster vs Apache Airflow.
has defined term: Compatibility
has defined term: Ease of Use
has defined term: Features
has defined term: Data Governance
has defined term: Deployment
has defined term: Pricing
has defined term: Maturity
has defined term: Community Adoption
has defined term: Developer Experience
has defined term: Cost
has defined term: Capability
has defined term: Learning Resources
has defined term: Infrastructure
has defined term: Enterprise
has defined term: Community
has defined term: Ecosystem
has defined term: Requirements
has defined term: Code Quality
has defined term: Production Readiness
has defined term: Integration
has defined term: Implementation Speed
has defined term: Transformation
has defined term: Cost Structure
has defined term: Monitoring
has defined term: Architecture
has defined term: Application Domain
has defined term: Learning
has defined term: Adoption
has defined term: Community & Adoption
has defined term: Onboarding
has defined term: Community Activity
has defined term: Community Support
has defined term: Performance & Scale
has defined term: Usability
has defined term: Speed
has defined term: Development
has defined term: Functionality
has defined term: Operations
has defined term: Productivity
has defined term: ML Ops Features
has defined term: Data Observability
has defined term: Adoption & Community
has defined term: Integrations
has defined term: Technical Requirements
has defined term: Scalability
has defined term: Data Quality
has defined term: Integration Ecosystem
has defined term: Accessibility
has defined term: Deployment & Setup
has defined term: Reliability
has defined term: Support & Licensing
has defined term: Language Support
has defined term: Extensibility
has defined term: Support
has defined term: Hardware
has defined term: Development Activity
has defined term: Features & Ecosystem
has defined term: System Requirements
has defined term: Performance
has defined term: Time to Production
has defined term: Data Reliability
has defined term: Learning Curve
has defined term: Setup
has defined term: Installation
has defined term: Production Use
has defined term: Community Health
has defined term: Maintenance
has defined term: Integration Breadth
has defined term: Market Demand
has defined term: Getting Started
has defined term: Enterprise Adoption

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Application Domain
type: DefinedTerm
term code: application-domain
in defined term set: Dagster vs Apache Airflow — Attribute Category Vocabulary

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

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

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

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

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

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

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

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

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

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

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

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

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

ML Ops Features
type: DefinedTerm
term code: ml-ops-features
in defined term set: Dagster vs Apache Airflow — Attribute Category Vocabulary

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Time to Production
type: DefinedTerm
term code: time-to-production
in defined term set: Dagster vs Apache Airflow — Attribute Category Vocabulary

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

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

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

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

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

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

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

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

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

Getting Started
type: DefinedTerm
term code: getting-started
in defined term set: Dagster vs Apache Airflow — Attribute Category Vocabulary

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

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T21:40:59.986Z
author: A Versus B
publisher: A Versus B
is part of: dagster-vs-airflow)
main entity: Which is easier to learn for someone new to orchestration?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which handles data lineage better?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can I migrate from Airflow to Dagster?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which is better for testing data pipelines?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which has better enterprise support and vendor options?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between Dagster and Apache Airflow
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Initial Release Year
step: Consider: Primary Paradigm
step: Consider: GitHub Stars
step: Consider: Built-in Type Safety
step: Consider: Data Lineage Tracking
step: Consider: Job Testing Framework
step: Consider: Production Deployments (reported)
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: Initial Release Year
type: HowToStep
position: 1
text: Initial Release Year: Dagster has 2019, while Apache Airflow has 2014.

Consider: Primary Paradigm
type: HowToStep
position: 2
text: Primary Paradigm: Dagster has Asset-oriented (focuses on data assets), while Apache Airflow has Task-oriented (focuses on DAGs/workflows).

Consider: GitHub Stars
type: HowToStep
position: 3
text: GitHub Stars: Dagster has 9,200+, while Apache Airflow has 35,000+.

Consider: Built-in Type Safety
type: HowToStep
position: 4
text: Built-in Type Safety: Dagster has Strong (Python type hints enforced), while Apache Airflow has Minimal (optional typing).

Consider: Data Lineage Tracking
type: HowToStep
position: 5
text: Data Lineage Tracking: Dagster has Native/first-class (asset lineage built-in), while Apache Airflow has Manual/third-party required.

Consider: Job Testing Framework
type: HowToStep
position: 6
text: Job Testing Framework: Dagster has Comprehensive in-process testing, while Apache Airflow has Limited, requires external setup.

Consider: Production Deployments (reported)
type: HowToStep
position: 7
text: Production Deployments (reported): Dagster has 2,000+ organizations, while Apache Airflow has 10,000+ organizations.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Number of Integrations
type: PropertyValue
value: Dagster: 100+ (Python, Spark, K8s, APIs, databases, cloud) | Apache Airflow: N/A

Learning Curve (Developer Hours)
type: PropertyValue
value: Dagster: 40-80 hours (requires Python, orchestration concepts) | Apache Airflow: N/A

Native Scheduling Support
type: PropertyValue
value: Dagster: Yes - built-in with Dagster Daemon | Apache Airflow: N/A

Native Monitoring & Alerting
type: PropertyValue
value: Dagster: Yes - built-in monitoring, alerting, and error tracking | Apache Airflow: N/A

Supported Programming Languages
type: PropertyValue
value: Dagster: Python, SQL, Spark, Go, shell scripts, APIs | Apache Airflow: N/A

Average Time to Deploy First Pipeline
type: PropertyValue
value: Dagster: 15-25 hours (setup + learning) | Apache Airflow: N/A

dbt Package Ecosystem Size
type: PropertyValue
value: Dagster: Not applicable | Apache Airflow: N/A

Time to First Tracked Experiment
type: PropertyValue
value: Dagster: 40-90 minutes | Apache Airflow: N/A

Model Registry Capabilities
type: PropertyValue
value: Dagster: Manual tracking via assets; no built-in staging workflow | Apache Airflow: N/A

Asset Lineage Tracking
type: PropertyValue
value: Dagster: Native asset graphs with automatic lineage, impact analysis, and upstream/downstream visibility | Apache Airflow: N/A

GitHub Stars
type: PropertyValue
value: Dagster: 9,500+ | Apache Airflow: 35,200
value reference: Apache Airflow

Supported ML Frameworks
type: PropertyValue
value: Dagster: Framework-agnostic via Python ops; requires custom integration | Apache Airflow: N/A

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

Maximum Concurrent Runs (single instance)
type: PropertyValue
value: Dagster: 500+ (optimized for high-volume asset materialization) | Apache Airflow: N/A

Type Safety & Validation
type: PropertyValue
value: Dagster: Built-in Python type hints with automatic validation and schema enforcement | Apache Airflow: Minimal type hints, runtime validation

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

Production Organizations (Reported)
type: PropertyValue
value: Dagster: 2,000+ | Apache Airflow: 10,000+
value reference: Apache Airflow

Available Providers/Operators
type: PropertyValue
value: Dagster: Limited (community growing) | Apache Airflow: 800+

Native Data Lineage Support
type: PropertyValue
value: Dagster: Yes (first-class asset lineage) | Apache Airflow: No (requires manual/external tooling)

Built-in Testing Framework
type: PropertyValue
value: Dagster: Yes (comprehensive in-process testing) | Apache Airflow: Limited (integration-focused)

Python Type Safety Support
type: PropertyValue
value: Dagster: Strong (enforced type hints) | Apache Airflow: Optional (not enforced)

Learning Curve Steepness
type: PropertyValue
value: Dagster: Moderate-to-High (asset-oriented paradigm) | Apache Airflow: Moderate (DAG-based, familiar to many)

Monthly PyPI/Package Downloads (2024)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 2.8M

Time to First Pipeline (expert user)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 8-16 hours

Native Data Warehouse Support
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 10+ via adapters

Minimum Python Knowledge Required
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Intermediate to Advanced

Open Source Contributors
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 1,200+

Core Use Case Scope
type: PropertyValue
value: Dagster: N/A | Apache Airflow: E, L, T, testing, ML, monitoring (full stack)

Infrastructure Setup Complexity
type: PropertyValue
value: Dagster: N/A | Apache Airflow: High (scheduler, web server, worker, database required)

Time to Production (First Workflow)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 120 minutes

Lines of Code (Basic ETL Task)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 50-70 lines

Minimum Infrastructure Requirements
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 4+ (scheduler, worker, DB, broker)

GitHub Stars (Community Indicator)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 35,000+ stars

Uptime SLA (Managed Services)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Self-hosted (varies)

Enterprise Support Availability
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Community or third-party paid

Configuration as Code Simplicity
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Complex (DAG operators)

GitHub Stars (Community Maturity)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 22,000+

Project Age
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 9+ years (since 2015)

Maximum Workflow Duration
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Days (practical limit)

Supported Programming Languages (SDKs)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Python (primary), Java/Go/C# (limited)

Pre-built Integrations/Operators
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 1,200+ official operators

Fault Tolerance Method
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Manual retry + task checkpointing

Minimum Deployment Complexity
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 5+ (scheduler, webserver, DB, executor, metadata)

Enterprise Commercial Support Available
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Yes (Astronomer, cloud providers)

Native Integrations
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 1,800+ Providers

Time to First Productive Workflow
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 5-10 days

Minimum RAM Requirement
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 1-2 GB

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

Annual Commit Activity
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 500+ commits

Dynamic DAG Support
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Yes (full support)

External Database Required
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Yes (PostgreSQL/MySQL)

Processing Latency
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 10,000-3,600,000 ms (10 seconds to 1 hour typical)

Maximum Throughput per Node
type: PropertyValue
value: Dagster: N/A | Apache Airflow: ~1,000-5,000 tasks/min

Time to Deploy Pipeline
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 5-15 minutes (quick setup)

Python Support Level
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Fully native (DAG definitions in pure Python)

State Consistency Guarantee
type: PropertyValue
value: Dagster: N/A | Apache Airflow: At-least-once (with retries)

Integrated Web UI
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Advanced (DAG viewer, logs, metrics, triggers)

Minimum Java Version Required
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Java 8+ (optional; Python primary)

Market Share Adoption
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 68%

Available Providers/Integrations
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 300+

Time to Proficiency
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 40-80

Cloud-Native Architecture
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Requires external components (Celery/Kubernetes/RabbitMQ)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Active Contributors (Last 12 Months)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 320+

Latest Stable Release
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 2.10+ (December 2024)

Setup Time (Beginner)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 45-60 (with Kubernetes/Celery)

Maximum Concurrency (Single Machine)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 32+ (configurable)

Metadata Database Requirement
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Required (PostgreSQL/MySQL/SQLite)

Number of Built-in Operators
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 300+

Job Listings on LinkedIn (2024)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 12,400+

Setup Time to Hello World
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 20-30 minutes

Baseline Memory Usage
type: PropertyValue
value: Dagster: N/A | Apache Airflow: ~350 MB

Built-in Web UI for Monitoring
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Yes (comprehensive dashboard included)

Maximum Supported Tasks per Workflow
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Recommended <5000 (performance degrades with larger DAGs)

Supported Brokers/Message Queues
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Primary: PostgreSQL, MySQL (uses database as queue)

GitHub Stars (2026)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: 36,200 stars

Task Retry Handling (native)
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Advanced (exponential backoff, custom retry policies, SLA alerts)

Production Deployments
type: PropertyValue
value: Dagster: N/A | Apache Airflow: Enterprise standard for data pipelines (Uber, Netflix, Airbnb, Google)

SpeakableSpecification
css selector: #short-answer
css selector: #verdict
css selector: #key-differences
css selector: #faq
xpath: //*[@id='short-answer']
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Dagster — Wikipedia
type: CreativeWork

Apache Airflow — Wikipedia
type: CreativeWork

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

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

Number of Integrations
type: PropertyValue
value reference: Functionality

Learning Curve (Developer Hours)
type: PropertyValue
value reference: Ease of Use

Native Scheduling Support
type: PropertyValue
value reference: Operations

Native Monitoring & Alerting
type: PropertyValue
value reference: Operations

Supported Programming Languages
type: PropertyValue
value reference: Compatibility

Average Time to Deploy First Pipeline
type: PropertyValue
value reference: Productivity

dbt Package Ecosystem Size
type: PropertyValue
value reference: Ecosystem

Time to First Tracked Experiment
type: PropertyValue
value reference: Ease of Use

Model Registry Capabilities
type: PropertyValue
value reference: ML Ops Features

Asset Lineage Tracking
type: PropertyValue
value reference: Data Observability

GitHub Stars
type: PropertyValue
value reference: Adoption & Community

Supported ML Frameworks
type: PropertyValue
value reference: Integrations

Minimum Python Version Required
type: PropertyValue
value reference: Technical Requirements

Maximum Concurrent Runs (single instance)
type: PropertyValue
value reference: Scalability

Type Safety & Validation
type: PropertyValue
value reference: Data Quality

Initial Release
type: PropertyValue
value reference: Maturity

Production Organizations (Reported)
type: PropertyValue
value reference: Adoption

Available Providers/Operators
type: PropertyValue
value reference: Integration Ecosystem

Native Data Lineage Support
type: PropertyValue
value reference: Features

Built-in Testing Framework
type: PropertyValue
value reference: Developer Experience

Python Type Safety Support
type: PropertyValue
value reference: Code Quality

Learning Curve Steepness
type: PropertyValue
value reference: Ease of Use

Monthly PyPI/Package Downloads (2024)
type: PropertyValue
value reference: Adoption

Time to First Pipeline (expert user)
type: PropertyValue
value reference: Ease of Use

Native Data Warehouse Support
type: PropertyValue
value reference: Integration

Minimum Python Knowledge Required
type: PropertyValue
value reference: Accessibility

Open Source Contributors
type: PropertyValue
value reference: Community

Core Use Case Scope
type: PropertyValue
value reference: Functionality

Infrastructure Setup Complexity
type: PropertyValue
value reference: Operations

Time to Production (First Workflow)
type: PropertyValue
value reference: Deployment & Setup

Lines of Code (Basic ETL Task)
type: PropertyValue
value reference: Development

Minimum Infrastructure Requirements
type: PropertyValue
value reference: Infrastructure

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

Uptime SLA (Managed Services)
type: PropertyValue
value reference: Reliability

Enterprise Support Availability
type: PropertyValue
value reference: Support & Licensing

Configuration as Code Simplicity
type: PropertyValue
value reference: Development

GitHub Stars (Community Maturity)
type: PropertyValue
value reference: Adoption & Community

Project Age
type: PropertyValue
value reference: Maturity

Maximum Workflow Duration
type: PropertyValue
value reference: Capability

Supported Programming Languages (SDKs)
type: PropertyValue
value reference: Language Support

Pre-built Integrations/Operators
type: PropertyValue
value reference: Extensibility

Fault Tolerance Method
type: PropertyValue
value reference: Reliability

Minimum Deployment Complexity
type: PropertyValue
value reference: Operations

Enterprise Commercial Support Available
type: PropertyValue
value reference: Support

Native Integrations
type: PropertyValue
value reference: Ecosystem

Time to First Productive Workflow
type: PropertyValue
value reference: Ease of Use

Minimum RAM Requirement
type: PropertyValue
value reference: Hardware

Built-in Web Dashboard
type: PropertyValue
value reference: Features

Annual Commit Activity
type: PropertyValue
value reference: Development Activity

Dynamic DAG Support
type: PropertyValue
value reference: Features & Ecosystem

External Database Required
type: PropertyValue
value reference: System Requirements

Processing Latency
type: PropertyValue
value reference: Performance

Maximum Throughput per Node
type: PropertyValue
value reference: Performance

Time to Deploy Pipeline
type: PropertyValue
value reference: Time to Production

Python Support Level
type: PropertyValue
value reference: Language Support

State Consistency Guarantee
type: PropertyValue
value reference: Data Reliability

Integrated Web UI
type: PropertyValue
value reference: Monitoring

Minimum Java Version Required
type: PropertyValue
value reference: Requirements

Market Share Adoption
type: PropertyValue
value reference: Adoption

Available Providers/Integrations
type: PropertyValue
value reference: Features

Time to Proficiency
type: PropertyValue
value reference: Learning Curve

Cloud-Native Architecture
type: PropertyValue
value reference: Architecture

Minimum Setup Complexity
type: PropertyValue
value reference: Setup

Enterprise Support Plans
type: PropertyValue
value reference: Support

Memory Usage at Idle
type: PropertyValue
value reference: Performance

Setup Time for Hello World
type: PropertyValue
value reference: Ease of Use

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

Task Dependency Management
type: PropertyValue
value reference: Features

Supported Message Brokers
type: PropertyValue
value reference: Features

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

Active Contributors
type: PropertyValue
value reference: Community

Minimum Database Setup
type: PropertyValue
value reference: Installation

Enterprise Production Adoption
type: PropertyValue
value reference: Production Use

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

Active Contributors (Last 12 Months)
type: PropertyValue
value reference: Community Health

Latest Stable Release
type: PropertyValue
value reference: Maintenance

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

Maximum Concurrency (Single Machine)
type: PropertyValue
value reference: Performance

Metadata Database Requirement
type: PropertyValue
value reference: Infrastructure

Number of Built-in Operators
type: PropertyValue
value reference: Integration Breadth

Job Listings on LinkedIn (2024)
type: PropertyValue
value reference: Market Demand

Setup Time to Hello World
type: PropertyValue
value reference: Getting Started

Baseline Memory Usage
type: PropertyValue
value reference: Performance

Built-in Web UI for Monitoring
type: PropertyValue
value reference: Features

Maximum Supported Tasks per Workflow
type: PropertyValue
value reference: Scalability

Supported Brokers/Message Queues
type: PropertyValue
value reference: Integration

GitHub Stars (2026)
type: PropertyValue
value reference: Community

Task Retry Handling (native)
type: PropertyValue
value reference: Features

Production Deployments
type: PropertyValue
value reference: Enterprise Adoption