Day 85 of 90 Days of DevOps Challenge: Enhance Monitoring using CloudWatch

Yesterday, on Day 84, I explored AWS Elastic Beanstalk, a powerful PaaS that simplifies application deployment by handling infrastructure provisioning, scaling, and monitoring behind the scenes. It helps developers focus more on code rather than managing servers.

Today, I’m diving into Amazon CloudWatch, the eyes and ears of AWS that provide deep visibility into resources, applications, and services running in the cloud.

What is AWS CloudWatch?

Amazon CloudWatch is a monitoring and observability service that collects metrics, logs, and events from AWS services and applications. It helps you understand system performance, optimize resource usage, detect issues, and respond to operational changes in real time.

CloudWatch acts as your control center for AWS environments.

Key Features

• Metrics Monitoring → Collects performance data like CPU, memory, disk, and network usage from AWS Resources

• CloudWatch Alarms → Set thresholds for metrics and trigger actions when breached.

• CloudWatch Logs → Centralized logging from applications, Lambda functions, or EC2 instances.

• CloudWatch Dashboards → Visualize metrics and logs in one unified view.

• CloudWatch Events (EventBridge) → Detect changes in resources and trigger automated workflows.

• Anomaly Detection → Uses ML models to detect unusual patterns in metrics.

• Cross-Account & Cross-Region Dashboards → Unified monitoring for multi-account architectures.

Why Do We Need CloudWatch?

In cloud environments, things can scale or fail in seconds. Without a monitoring system, it’s impossible to stay on top of it all. CloudWatch helps by:

• Giving visibility into system performance.

• Alerting proactively before end users are affected.

• Automating responses like scaling or restarting instances.

• Optimizing costs by showing underutilized resources.

• Ensuring compliance & security with centralized logging.

• Supporting hybrid monitoring for on-prem + AWS.

Common Use Cases

• Monitor EC2 CPU utilization and trigger Auto Scaling.

• Track RDS performance metrics such as queries, connections, and latency.

• Log and analyze application errors in CloudWatch Logs.

• Send SNS alerts for billing thresholds or anomalies.

• Trigger a Lambda function when a CloudWatch Event detects unusual activity.

Pricing

Pay-per-use model based on:

• Metrics collected

• API requests

• Log ingestion and storage

• Dashboards and alarms

Advantages of CloudWatch

• Fully managed, no setup overhead.

• Deep integration with the AWS ecosystem.

• Near real-time monitoring & alerts.

• Helps with auditing and compliance.

• Supports both infrastructure and application metrics.

Limitations

• Can become costly if log retention and custom metrics aren’t optimized.

• Needs careful alarm design to avoid alert fatigue.

• Dashboards/queries have a bit of a learning curve.

Real-World Example

Suppose you host a web application on EC2.

• CloudWatch tracks CPU Utilization.

• You create an Alarm that triggers if CPU > 80% for 5 minutes.

• The alarm sends an SNS notification to the Ops team.

• At the same time, it triggers Auto Scaling to launch a new EC2 instance.

Result → The system auto-heals, scales, and notifies the team without manual intervention.

Final Thoughts

CloudWatch is more than just a monitoring tool. it’s a complete observability solution. It enables teams to stay proactive, ensure uptime, and automate responses to system changes.

By mastering CloudWatch, you gain the ability to monitor, troubleshoot, and optimize applications at scale, making it a critical skill for any cloud engineer.

Tomorrow, I’ll explore AWS CloudTrail, which ensures governance, compliance, and auditability across AWS accounts.