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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! ββ 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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by Tobi & Sandro
Join our community of over 8,800 readers delving into AWS. We highlight real-world best practices through easy-to-understand visualizations and one-pagers. Expect a fresh newsletter edition every two weeks.
Hi Reader and happy May the 4th β. We want to start the spring by giving you an amazing discount on our book and other resources. For that, we have partnered with four different AWS Creators around the globe. You can use the code AWSFUND30 to get 30% off. The resources are: π AWS Fundamentals - Like this newsletter, it covers the basics of essential AWS Services for real-world applications. π The DynamoDB Book - Level up your DynamoDB modeling and finally understand Single-Table Design. ποΈ...
β Reading time: 6.3 minutes π Main Learning: Working with the Bedrock API π¨π½π» GitHub Code π Blog Post Hey Reader ππ½ in this newsletter, weβll explore how to build a serverless chat application that uses Amazon Bedrock and the OpenAI API. Weβll use SST (Serverless Stack) to develop and deploy the application on AWS, featuring Next.JS for the frontend and DynamoDB and Lambda for backend services. π‘ The application's full repository can be found on our Github organization. You can deploy it with...
β Reading time: 6.3 minutes π Main Learning: Observability Aggregation with OAM π¨π½π» GitHub Code π Blog Post Hey Reader ππ½ Ever tried setting up an AWS Landing Zone? If you have, you know it's not easy. AWS recommends using a separate account just for monitoring all your log data. We're here to introduce the AWS Observability Access Manager (OAM), designed to make this task easier. Previously, we couldn't use OAM effectively due to a major limitation, but that's changed. Interested in diving...