[PROPOSAL] 🚀 AI-Powered Spark Debugging & Optimization - Game Changers for Kubernetes Users

[vara....@gmail.com]

Hi Kubeflow Community,

Calling all Spark on Kubernetes practitioners! If you're wrestling with Spark History Server for event monitoring and debugging, we've got something exciting to share.

We're revolutionizing how you work with Spark on Kubernetes through AI.

🔥 Two groundbreaking projects launching:

1/ Spark History Server MCP Server (AWS-led, going open source)
• Seamless integration with your existing monitoring workflows
• Enhanced observability and debugging capabilities

2/ SparkSense-AI - Your intelligent Spark companion (AWS-led, going open source)
• AI agent that automatically debugs and optimizes Spark jobs
• Real-time performance insights and recommendations
• Built specifically for Kubernetes environments

Why should you care? These tools will transform how you:

• Debug failed Spark jobs (minutes instead of days)
• Optimize resource utilization automatically
• Monitor performance across your Kubernetes clusters

Let's make this interactive! We can demo both solutions or focus deep on one - whatever serves the community best. Multiple sessions? Absolutely, if there's interest.

Ready to see AI supercharge your Spark workflows?

Cheers,
Vara Bonthu

Kubeflow Spark Operator Maintainer

Principal Open Source Specialist @ AWS

[vara....@gmail.com]

Forgot to mention that we are planning to demo this on July 8th 8am PST.

[RShekhar Prasad]

Yes, there's a strong appeal in using language models to simplify log debugging and monitoring.
Thank you for sharing—I'd be excited to explore the open-source GitHub repository and contribute to making it even more powerful.

Thanks and regards,

Shekhar Prasad Rajak,

Blog | Github | Twitter

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