What problem does it solve? Operating Amazon MSK Provisioned clusters requires distinguishing between Standard and Express broker types, which behave differently across sizing, storage, maintenance, and monitoring — and generic Kafka knowledge frequently conflates them, leading to wrong recommendations. ## Core Features & Use Cases - Performance and Lag Troubleshooting: Diagnose high CPU, latency, traffic shaping, consumer lag, and rebalance storms with broker-type-specific guidance. - Sizing and Cost Estimation: Run the bundled msk_sizing.py script to compute broker counts, instance choices, and monthly costs for Standard vs Express clusters. - Storage, Monitoring, and Maintenance: Manage EBS expansion and tiered storage on Standard, monitor StorageUsed on Express, configure CloudWatch alarms, and handle patching, upgrades, and rolling restarts. - Streaming Delivery: Set up Streaming Tables to S3 Tables (Iceberg) and Data Delivery to general-purpose S3 buckets on Express brokers, including IAM policies and channel management. - Use Case: A user asks why consumer lag is growing on their MSK cluster; the skill identifies the broker type, walks through lag diagnosis, and recommends client configuration and alarm fixes. ## Quick Start Ask the assistant to diagnose high consumer lag on your Amazon MSK cluster and recommend the right broker size and CloudWatch alarms.
