Azure Data Factory vs Apache Airflow
Developers should learn Azure Data Factory when building data pipelines in the Azure ecosystem, especially for scenarios requiring scalable, serverless data integration across cloud and on-premises environments meets developers should learn apache airflow when building, automating, and managing data engineering pipelines, etl processes, or batch jobs that require scheduling, monitoring, and dependency management. Here's our take.
Azure Data Factory
Developers should learn Azure Data Factory when building data pipelines in the Azure ecosystem, especially for scenarios requiring scalable, serverless data integration across cloud and on-premises environments
Azure Data Factory
Nice PickDevelopers should learn Azure Data Factory when building data pipelines in the Azure ecosystem, especially for scenarios requiring scalable, serverless data integration across cloud and on-premises environments
Pros
- +It is ideal for ETL/ELT processes, data migration projects, and orchestrating big data workflows, as it simplifies data ingestion from sources like databases, files, and SaaS applications, and transforms data using Azure Databricks or HDInsight
- +Related to: azure-synapse-analytics, azure-databricks
Cons
- -Specific tradeoffs depend on your use case
Apache Airflow
Developers should learn Apache Airflow when building, automating, and managing data engineering pipelines, ETL processes, or batch jobs that require scheduling, monitoring, and dependency management
Pros
- +It is particularly useful in scenarios involving data integration, machine learning workflows, and cloud-based data processing, as it offers scalability, fault tolerance, and integration with tools like Apache Spark, Kubernetes, and cloud services
- +Related to: python, data-pipelines
Cons
- -Specific tradeoffs depend on your use case
The Verdict
Use Azure Data Factory if: You want it is ideal for etl/elt processes, data migration projects, and orchestrating big data workflows, as it simplifies data ingestion from sources like databases, files, and saas applications, and transforms data using azure databricks or hdinsight and can live with specific tradeoffs depend on your use case.
Use Apache Airflow if: You prioritize it is particularly useful in scenarios involving data integration, machine learning workflows, and cloud-based data processing, as it offers scalability, fault tolerance, and integration with tools like apache spark, kubernetes, and cloud services over what Azure Data Factory offers.
Developers should learn Azure Data Factory when building data pipelines in the Azure ecosystem, especially for scenarios requiring scalable, serverless data integration across cloud and on-premises environments
Disagree with our pick? nice@nicepick.dev