Detect and resolve HBase inconsistencies faster with AI on Amazon EMR

This article demonstrates building an AI-powered HBase troubleshooting solution using Amazon OpenSearch Service and Kiro CLI to dramatically reduce incident resolution time.

• Reduces HBase inconsistency resolution from hours to minutes using AI analysis
• Combines Amazon OpenSearch vector search with Amazon Bedrock for semantic log analysis
• Processes HBase logs, HBCK reports, and metadata with sentence transformer embeddings
• Enables natural language queries for root cause identification across operational data
• Deploys via AWS CloudFormation with Amazon EMR, EC2, and OpenSearch integration
• Supports custom knowledge bases through Git repository ingestion via Kiro CLI
• Implements least-privilege IAM roles and VPC security best practices
• Detects orphan regions, missing metadata, rowkey holes, and stuck region transitions

The solution democratizes HBase troubleshooting by automating log correlation and analysis, reducing dependency on specialized expertise while improving operational efficiency and MTTR.