Dagster is a Python-native data orchestrator for complex, modern data pipelines. It emphasizes local testability, a strong focus on asset-based dependencies, and an intuitive UI for monitoring. Unlike task-centric tools like Airflow, Dagster's core abstractions of 'ops', 'assets', and 'resources' facilitate code-native pipeline definitions that integrate better with your tech stack (e.g., dbt, ML libraries). This shift enables cleaner development, testability, and greater data platform reliability. While other tools force ETL to conform to workflow patterns, Dagster's model allows those data workflows to be developed organically from Python development habits.
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Why is Dagster better on Shakudo?
Why is Dagster better on Shakudo?
Why deploy Dagster with Shakudo?
Stress-Free infrastructure
Deploy Shakudo easily on your VPC, on-premise, or on our managed infrastructure, and use the best data and AI tools the next day.
Integrate with everything
Empower your team with seamless integration to the most popular data and AI frameworks and tools they want to use.
Streamlined Workflow
Automate your DevOps completely with Shakudo, so that you can focus on building and launching solutions.
Use data and AI products inside your infrastructure
Chat with one of our experts to answer your questions about your data stack, data tools you need, and deploying Shakudo on your cloud.