Amazon SageMaker Unified Studio MCP for Spark Upgrade

Category: Cloud & DevOps – MCP Servers
Brand: Amazon Web Services (AWS)
Website: https://modelcontextprotocol.io/mcp/servers/sagemaker-unified-studio-spark-upgrade-mcp-server

Note: The source page currently returns a 404, so details are inferred from the provided item description only.

Overview

The Amazon SageMaker Unified Studio MCP for Spark Upgrade is an MCP-compatible server that exposes Apache Spark upgrade and migration tooling through SageMaker Unified Studio. It is intended to help modernize existing Spark applications and clusters and migrate them to AWS managed services such as AWS Glue and Amazon EMR, using standardized Model Context Protocol (MCP) interfaces.

Features

• MCP server for Spark modernization
  Provides an MCP server that tools and MCP clients can connect to in order to drive Spark upgrade and migration workflows.

• Apache Spark upgrade tooling
  Offers capabilities (via MCP interfaces) aimed at upgrading existing Apache Spark code and environments to newer Spark versions.

• Migration support for Spark applications
  Focuses on helping move existing Spark applications to AWS services, including guidance or automation for refactoring and compatibility.

• Cluster migration and modernization
  Targets migration of Spark clusters to AWS-managed platforms, reducing the need to operate self-managed clusters.

• Integration with SageMaker Unified Studio
  Runs within or alongside SageMaker Unified Studio, enabling Spark upgrade and migration workflows inside a unified development and operations environment on AWS.

• Targets AWS Glue and Amazon EMR
  Helps modernize and migrate Spark workloads specifically onto AWS Glue and Amazon EMR, aligning Spark jobs with AWS-native data processing services.

• MCP-based interfaces
  Exposes its capabilities through Model Context Protocol interfaces, allowing compatible clients (such as AI assistants or developer tools that speak MCP) to programmatically interact with the Spark upgrade and migration tooling.

Use Cases

• Modernizing legacy Spark applications to newer Spark runtimes on AWS.
• Migrating on-premise or self-managed Spark clusters to AWS Glue or Amazon EMR.
• Integrating Spark upgrade workflows into MCP-enabled development tools or AI agents.

Pricing

Pricing information is not provided in the available content. Refer to the official AWS or MCP server documentation for details on costs, if any.