Ojasa Mirai

Ojasa Mirai

Cloud

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Learning Level

🟢 Beginner🔵 Advanced
🔧 AWS Account Setup⚙️ AWS Compute Overview🖥️ EC2 Deployment🎯 Elastic Beanstalk⚡ Lambda Serverless📁 S3 Static Hosting🗄️ RDS Relational Database📊 DynamoDB NoSQL💾 ElastiCache Caching📊 AWS Monitoring🔑 AWS Authentication📈 AWS Scaling & Load Balancing🐳 AWS ECS Containers💰 AWS Cost Optimization
Cloud/Aws Deployment/Aws Monitoring

📊 AWS Monitoring

Introduction

AWS CloudWatch provides comprehensive monitoring, logging, and alerting for AWS resources.

Key Learning Outcomes

By the end of this lesson, you'll understand:

  • CloudWatch metrics and dashboards
  • Logs and log groups
  • Setting up alarms
  • X-Ray tracing
  • Dashboards and visualizations
  • Cost tracking

Creating Metrics

# Put custom metric
aws cloudwatch put-metric-data \
  --namespace MyApp \
  --metric-name ProcessingTime \
  --value 100 \
  --unit Milliseconds

Creating Alarms

# Create alarm
aws cloudwatch put-metric-alarm \
  --alarm-name "High CPU" \
  --alarm-description "Alert when CPU > 80%" \
  --metric-name CPUUtilization \
  --namespace AWS/EC2 \
  --statistic Average \
  --period 300 \
  --evaluation-periods 1 \
  --threshold 80 \
  --comparison-operator GreaterThanThreshold

Logs

# Create log group
aws logs create-log-group --log-group-name /aws/lambda/my-function

# Put log events
aws logs put-log-events \
  --log-group-name /aws/lambda/my-function \
  --log-stream-name 2024-01-01 \
  --log-events timestamp=123,message="Error occurred"

Dashboards

# Create dashboard
aws cloudwatch put-dashboard \
  --dashboard-name MyDashboard \
  --dashboard-body file://dashboard.json

Key Takeaways

  • **CloudWatch** monitors all AWS resources
  • **Metrics** track performance indicators
  • **Logs** provide detailed diagnostics
  • **Alarms** notify on anomalies
  • **Dashboards** visualize key metrics

Next Steps

Learn about authentication and access control, or explore scaling strategies.


Resources

Python Docs

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