Happy New Year, Reader! π 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
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 (β ) 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 ChapterDavid Yanacek (Engineer @CloudWatch) has an amazing talk about Observability from the last re:Invent. We recommend you check this one out! β 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 LaunchesAmazon 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:
In our book's application, we'll also go in-depth with Evidently by implementing several feature flags that steer some features. 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:
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Hey Reader First things first: we apologize for not providing updates on The CloudWatch Book for a while! π’ Sometimes, things don't go as planned and unexpected obstacles arise. But now, we're back in action, creating videos and putting the final touches on the book's content! π₯ Don't just take our word for it! As an early subscriber, here's a free video from one of our favorite chapters: Anomaly Detection π In this deep-dive, you'll learn how to detect unusual patterns in metrics without...
β Reading time: 11.5 minutes π Main Learning: Host Web Applications on AWS with the CloudFront Hosting Toolkit π¨π½π» GitHub Code π Blog Post Hey Reader ππ½ Happy New Week! I (Sandro) will attend the Serverless Days in Milano next week where Jeremy Daley will hold the keynote. I look forward to meeting many of you and the overall AWS community. This week's newsletter is all about hosting your frontend on AWS. AWS launched a new way of deploying your frontend to it: The CloudFront Hosting Toolkit....
β Reading time: 7 minutes π Main Learning: Visual Regression Testing with AWS CloudWatch Synthetics π¨π½π» GitHub Code π Blog Post Hey Reader Keeping your website running smoothly is important.It's also important to keep an eye on its visual appearance and that it doesn't change due to an unwanted change. AWS CloudWatch Synthetic Canaries is a tool that helps you check on your websites (and APIs!) to make sure itβs working well and look as expected. In this newsletter, weβre going to show you...