Big Data Platform - Amazon EMR - AWS (original) (raw)

Amazon EMR combines performance-optimized Apache Spark for faster, cost-efficient processing with the flexibility to choose instance types, including Spot Instances, and fully managed automatic scaling that dynamically right-sizes cluster—eliminating over-provisioning and reducing overall spend.

Amazon EMR is up 5.4x faster than open-source Apache Spark while maintaining API compatibility. It enables customers to deploy open- source frameworks of their choice – Apache Spark, Trino, Apache Flink, or Apache Hive. EMR supports popular open table formats like Iceberg, Hudi and Delta to accelerate time-to-insight.

EMR offers choice in deployment, including EMR Serverless for fully managed, infrastructure-free processing, EMR on EC2 for fine-grained cluster control, and EMR on EKS for Kubernetes native big data workloads. Whether running short-term clusters for on-demand jobs or long-running clusters for persistent tasks, EMR adapts to your operational needs while optimizing costs through flexible resource allocation and efficient scaling.

Amazon EMR in the next generation of Amazon SageMaker empowers you to run open-source frameworks like Apache Spark, Trino, and Apache Flink, allowing you to scale analytics workloads effortlessly—all without provisioning or managing infrastructure. With EMR’s capabilities in Amazon SageMaker, you can unify data processing and model development, enabling end-to-end workflows from raw data transformation to AI deployment in a single collaborative environment.

Transform months-long Apache Spark upgrades into efficient week-long projects through intelligent automation. The Spark upgrade agent streamlines enterprise-scale migrations by automatically analyzing and validating API changes across your entire codebase, significantly reducing both cost and complexity.