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Introducing Data on EKS – Modernize Data Workloads on Amazon EKS


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We are thrilled to introduce Data on EKS (DoEKS), a new open-source project aimed at streamlining and accelerating the process of building, deploying, and scaling data workloads on Amazon Elastic Kubernetes Service (Amazon EKS).

With DoEKS, customers get access to a comprehensive range of resources including Infrastructure as Code (IaC) templates, performance benchmark reports, deployment examples, and architectures optimized for data-centric workloads aligned with AWS best practices and industry expertise. This means that customers can quickly and easily provision popular open-source data frameworks (e.g., Apache Spark, Ray, Apache Airflow, Argo Workflows, and Kubeflow) to run on Amazon EKS. Additionally, DoEKS areas of focus include distributed streaming platforms, query engines, and databases to meet the growing demands of data processing. DoEKS blueprints are made with managed AWS services and popular open-source tools to provide customers flexibility to choose the right combination of managed and self-managed components to suit their needs. For example, DoEKS includes several blueprints with Amazon EMR on EKS so customers can take advantage of optimized features like automated provisioning, scaling, faster runtimes, and debugging tools that Amazon EMR provides for running Spark applications...

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This diagram shows end-to-end focus areas of Data on EKS. Managed and Self-Managed tools included.

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