Airflow vs Dagster
Developers should learn Airflow when building and managing data engineering pipelines, ETL processes, or any automated workflows that require scheduling, monitoring, and error handling meets developers should learn dagster when building complex, maintainable data pipelines that require strong typing, testing, and observability, such as in data engineering, machine learning operations (mlops), or etl/elt processes. Here's our take.
Airflow
Developers should learn Airflow when building and managing data engineering pipelines, ETL processes, or any automated workflows that require scheduling, monitoring, and error handling
Airflow
Nice PickDevelopers should learn Airflow when building and managing data engineering pipelines, ETL processes, or any automated workflows that require scheduling, monitoring, and error handling
Pros
- +It is particularly useful in data-intensive applications, such as data warehousing, machine learning pipelines, and business intelligence reporting, where tasks need to be orchestrated reliably and scalably
- +Related to: python, dag
Cons
- -Specific tradeoffs depend on your use case
Dagster
Developers should learn Dagster when building complex, maintainable data pipelines that require strong typing, testing, and observability, such as in data engineering, machine learning operations (MLOps), or ETL/ELT processes
Pros
- +It is particularly useful in scenarios where data quality and lineage tracking are critical, as it integrates seamlessly with tools like dbt, Apache Airflow, and cloud data warehouses, enabling teams to manage dependencies and configurations declaratively
- +Related to: apache-airflow, prefect
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Airflow if: You want it is particularly useful in data-intensive applications, such as data warehousing, machine learning pipelines, and business intelligence reporting, where tasks need to be orchestrated reliably and scalably and can live with specific tradeoffs depend on your use case.
Use Dagster if: You prioritize it is particularly useful in scenarios where data quality and lineage tracking are critical, as it integrates seamlessly with tools like dbt, apache airflow, and cloud data warehouses, enabling teams to manage dependencies and configurations declaratively over what Airflow offers.
Developers should learn Airflow when building and managing data engineering pipelines, ETL processes, or any automated workflows that require scheduling, monitoring, and error handling
Disagree with our pick? nice@nicepick.dev