AWS emr documentation change
Summary
Updated supported platforms for Spark Troubleshooting Agent to include EMR deployment options.
Security assessment
The change expands product coverage documentation without referencing security features or vulnerabilities. It's a routine update clarifying deployment scope.
Diff
diff --git a/emr/latest/ReleaseGuide/spark-troubleshoot.md b/emr/latest/ReleaseGuide/spark-troubleshoot.md index aa365410e..425100f29 100644 --- a//emr/latest/ReleaseGuide/spark-troubleshoot.md +++ b//emr/latest/ReleaseGuide/spark-troubleshoot.md @@ -13 +13 @@ IntroductionArchitecture Overview -The Apache Spark Troubleshooting Agent for Amazon EMR is a conversational AI capability that simplifies the troubleshooting of Apache Spark applications on Amazon EMR, AWS Glue and Amazon SageMaker Notebooks. Traditional Spark troubleshooting requires extensive manual analysis of logs, performance metrics, and error patterns to identify root causes and code fixes. The agent simplifies this process through natural language prompts, automated workload analysis, and intelligent code recommendations. +The Apache Spark Troubleshooting Agent for Amazon EMR is a conversational AI capability that simplifies the troubleshooting of Apache Spark applications on Amazon EMR (EMR on EC2, EMR Serverless, and EMR on EKS), AWS Glue and Amazon SageMaker Notebooks. Traditional Spark troubleshooting requires extensive manual analysis of logs, performance metrics, and error patterns to identify root causes and code fixes. The agent simplifies this process through natural language prompts, automated workload analysis, and intelligent code recommendations. @@ -23 +23 @@ The Apache Spark Troubleshooting Agent is available at no additional cost as par -The troubleshooting agent has three main components: an MCP-compatible AI Assistant in your development environment for interaction, the [MCP Proxy for AWS](https://github.com/aws/mcp-proxy-for-aws) that handles secure communication and authentication between your client and AWS services, and the Amazon SageMaker Unified Studio Remote MCP Server that provides specialized Spark troubleshooting tools for Amazon EMR, AWS Glue and Amazon SageMaker Notebooks. This diagram illustrates how you interact with the Amazon SageMaker Unified Studio Remote MCP Server through your AI Assistant. +The troubleshooting agent has three main components: an MCP-compatible AI Assistant in your development environment for interaction, the [MCP Proxy for AWS](https://github.com/aws/mcp-proxy-for-aws) that handles secure communication and authentication between your client and AWS services, and the Amazon SageMaker Unified Studio Remote MCP Server that provides specialized Spark troubleshooting tools for Amazon EMR (EMR on EC2, EMR Serverless, and EMR on EKS), AWS Glue and Amazon SageMaker Notebooks. This diagram illustrates how you interact with the Amazon SageMaker Unified Studio Remote MCP Server through your AI Assistant.