This chapter introduces Amazon CloudWatch, its core services, and the principles of observability and monitoring.

Getting started with Amazon CloudWatch

Take a tour of the CloudWatch console

What are monitoring and observability?

Core CloudWatch functionality

CloudWatch metrics

Classify metric components

Custom metrics and dimensions

Explore metric anatomy

Building CloudWatch dashboards

Spot the performance bottleneck

Create a CloudWatch dashboard

Explore dashboard widgets

CloudWatch Monitoring and Observability

Learn about how to work with CloudWatch logs and setup alarms and notifications.

CloudWatch Logs

Create a metric filter

Explore log architecture

Classify log hierarchy components

CloudWatch alarms and notifications

Create a CloudWatch alarm

Classify alarm states and actions

Simulate alarm evaluation

SNS and SQS integration with CloudWatch

Send a message to an SQS queue

Explore SNS and SQS notification flow

Classify SNS vs SQS

Log Insights and analysis

Run a Log Insights query

Match troubleshooting scenarios to queries

Explore Log Insights queries

CloudWatch Logs and Alarms

Learn how to observe distributed applications using AWS X-Ray.

X-Ray concepts and architecture

Simulate X-Ray sampling

Distributed trace lifecycle

Order the lifecycle of a distributed trace

How X-Ray represents downstream calls

Implementing X-Ray tracing

Annotation or metadata for filtering?

Options

Automatic instrumentation

Manual instrumentation

Automatic vs manual instrumentation

Flask instrumentation steps

Order the steps to instrument a Flask app

Analyzing traces and service maps

Read an X-Ray service map

Sequential bottleneck

N+1 queries / chatty services

Cold start

Match the latency anti-pattern to its fix

Diagnose the latency pattern

Application health dashboards

Latency

Traffic

Errors

Saturation

Classify metrics into the four golden signals

Troubleshooting workflow

Order the seven-step troubleshooting workflow

Build an application health dashboard

Congratulations!

Distributed Tracing with X-Ray

When an AWS application slows down or starts failing, can you find the cause before your users do? This course gives you the observability skills to monitor, diagnose, and fix production workloads with confidence. With Amazon CloudWatch, you'll collect metrics and logs, build dashboards that surface problems at a glance, and set up alarms and notifications that alert you the moment something breaks. With AWS X-Ray, you'll trace requests across distributed services, read service maps, and pinpoint the exact hop driving latency or errors. You'll work through realistic troubleshooting scenarios, including failed Lambda invocations, slow database calls, and cascading failures, turning guesswork into a repeatable diagnostic process. By the end, you'll keep AWS applications reliable, performant, and observable from end to end.

AWS Concepts

Monitor and troubleshoot AWS workloads with Amazon CloudWatch and AWS X-Ray: collect metrics and logs, build dashboards, set alarms, and trace requests.

Monitoring and troubleshooting AWS

Monitor and troubleshoot AWS apps with Amazon CloudWatch and AWS X-Ray. Collect metrics and logs, build dashboards, set alarms, and trace requests.

Calling patch_all() to instrument all supported libraries

Calling patch(['boto3', 'requests']) for selected libraries

Wrapping a function with @xray_recorder.capture()

Using a with xray_recorder.capture() context manager

Explicitly adding a named subsegment around a code block

Automatic vs manual instrumentation

Interaktive praktische Übung