Data orchestration platform for ML pipelines
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TL;DR - Dagster
- Modern data pipeline tool
- Declarative YAML configurations
- Built-in data quality checks
Pricing: Paid only
Best for: Enterprises & pros
Pros & Cons
Pros
- Modern data orchestration
- Good DX
- Asset-centric
- Type system
- Good documentation
Cons
- Learning curve
- Newer than Airflow
- Cloud expensive
- Ecosystem smaller
- Resource intensive
Key Features
Data orchestrationAssetsLineageObservabilityML pipelinesOpen source
Pricing Plans
30-day Free TrialOpen Source
Usage-based pricing
- Full orchestration
- All features
- Community support
Solo
$10/month
Cloud
- 7,500 free credits
- $0.04/extra credit
- 1 user
Most Popular
Starter
Teams
- 30,000 free credits
- $0.03/extra credit
- Multiple users
Pro
Enterprise
- Custom credits
- SLA
- Dedicated support
What is Dagster?
Dagster orchestrates data pipelines with a focus on developer experience. Define assets and their dependencies, let Dagster handle scheduling and execution-data engineering that fits software engineering practices.
The asset-based model aligns with how data teams think. Observability is built in. Testing and development workflows feel natural.
Data teams wanting modern tooling for pipeline orchestration choose Dagster for data engineering that applies software engineering discipline.
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Dagster FAQ
Is Dagster free?
Yes, open source under Apache 2.0. Dagster Cloud starts at $100/month. Self-hosting is completely free.
What is Dagster?
Dagster is a data orchestration platform. Define data pipelines as code with software engineering practices. Modern alternative to Airflow.
Dagster vs Airflow?
Dagster has better developer experience and asset-based approach. Airflow is more mature with larger ecosystem. Dagster for modern data teams.
What are Dagster assets?
Assets represent data you care about (tables, ML models, etc.). Define how to produce assets, Dagster handles orchestration. Different mental model from tasks.
Source: dagster.io