Happy New Year, Reader! 🎉

We hope you had some great holidays and could recharge with your loved ones.

In this update, we will show you the table of contents of the CloudWatch Book and give you some insights into a lesser-known feature of CloudWatch - Evidently.

As always, we love to hear your feedback. If you have any topics that you are missing or think are completely useless, let us know!

Table of Contents

1. Introduction ✅
2. Basics of CloudWatch 🏗️
3. Example Project - GitHub Repository Tracker ✅
4. Logs and Insights ✅
5. Metrics ✅
6. Alarms ✅
7. Dashboards ✅
8. X-Ray ✅
9. Synthetics ✅
10. Real-User Monitoring
11. Evidently
12. Anomaly Detection
13. Integrations with third parties
14. CloudWatch for Enterprises

These are all the high-level chapters. The major part of the book will be in Chapters 4 - 8. Because we think these are the most important ones.

All checked (✅) chapters are already done, in an initial draft version. Everything else is still in the works.

The chapters are not pure theory! They will include a lot of code and examples from the example project. You can use your own AWS Account and reproduce everything we explain in the book.

Additional Chapter

David Yanacek (Engineer @CloudWatch) has an amazing talk about Observability from the last re:Invent. We recommend you check this one out!

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He structured his talk around diagnosing issues, uncovering hidden issues, and preventing future issues. This got us thinking about adding a second part to the book. The second part would all be about applying the CloudWatch Fundamentals on detecting, finding, and preventing issues.

Let's dive into one small part of the upcoming book: CloudWatch Evidently.

Evidently - Feature Flags and Dark Launches

Amazon CloudWatch Evidently is a feature within the CloudWatch suite designed to help you run experiments and gain insights into your experimental features or proof-of-concepts before releasing them to the full audience of your application.

You can decide which features are activated for which part of your users and you can measure the impact by collecting metrics.

The key components of Evidently are:

• Feature Flags: Feature flags allow developers to toggle features on and off without deploying new code. This capability facilitates safe testing or running A/B tests in production environments, enabling a gradual rollout of features to mitigate risk.
• Experiments: Evidently supports A/B testing to make data-driven decisions. Developers can create experiments to test feature impact hypotheses, comparing variations against control groups.
  To launch an experiment in Evidently, define the feature, segment your audience, and set the metrics for evaluation. Evidently will then allocate traffic to different variations and collect data for analysis.
• Metrics and Analysis: Evidently provides detailed metrics and analysis tools. It tracks experiment results and feature flag impacts on application performance and user behavior, offering insights through customizable dashboards.

In our book's application, we'll also go in-depth with Evidently by implementing several feature flags that steer some features.
For example, we can decide if certain information about our favorite repositories, e.g. the number of stars, are being displayed.

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We'll also show you how Evidently helps to collect metrics on the different feature flag configurations so we can make sense of the experiments we run.

Previous Updates

Our goal is to let you participate in the creation of the CloudWatch Book as best as possible. For that, we want to send 1-2 emails per month. If you've missed the last ones, you can find them here:

• 10th December: Inside the CloudWatch Book 📕​

Thank you for joining the journey of the CloudWatch Book! If you only want to receive the AWS Newsletter and not these updates anymore, please update your preferences here.

Best ✌🏽
Sandro & Tobi

Tobi & Sandro our goal is to simplify AWS & Cloud Learning for everybody. You don't need expensive certifications to build on AWS! AWS Fundamentals Blog​

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