Run Spark Jobs on AWS-EMR with local Airflow Dag Setup


Unlock the power of Apache Airflow and Apache Spark with our seamless integration, allowing you to effortlessly run Spark jobs on your remote EMR cluster right from your local machine. Our ready-to-use setup simplifies the process, empowering you to focus on your data workflows rather than complex configurations. Here’s what you can expect from our product:

  • Plug-and-Play Setup: Say goodbye to tedious configurations. Our Apache Airflow DAG setup comes pre-configured, enabling you to get started with running Spark jobs on EMR in 5 simple steps.
  • Local Control, Remote Execution: Enjoy the convenience of managing your Spark jobs locally while leveraging the computing power of your EMR cluster. With our solution, you can seamlessly orchestrate your workflows without leaving your development environment.
  • Scalability and Reliability: Leverage the scalability and reliability of Amazon EMR for processing large-scale data workloads. Our integration ensures your Spark jobs run efficiently, regardless of the volume of data.
  • Cost-Effective Solution: Optimize your costs by utilizing the resources of your EMR cluster only when needed. With Apache Airflow’s scheduling capabilities, you can run jobs during off-peak hours, maximizing cost efficiency.
  • Comprehensive Documentation: Our product comes with comprehensive documentation, ensuring you have all the resources you need to make the most out of your Spark and Airflow integration.
SKU: GKBP0003 Category:


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Code only, Code + Ubuntu VM