Securing an endpoint used to be hard. Nowadays, with the cloud, it’s quite easy. You only need to know how! Assume you have files on Amazon S3 that you like to share. You could make the object publicly available. This would allow your users to download the file using their browsers simply. If you need to scale it, you can add Amazon CloudFront. This would cache the content closer to your users, making sure that your users have the best performance. But what if you want to control who can download the file? For this, you will need authentication and authorization.

Writing documentation sucks! But there are ways to make it easier and maybe even fun! In one of my previous blogs, I explained how you can embed your documentation in your pull requests and why you should consider doing it, too. In this blog, I want to go a bit deeper into the syntax you can use to improve your documentation pages on Confluence.

When running a Docker container on Amazon Elastic Container Service and AWS Fargate, persistent storage is often a necessity. I initially attempted to solve this by manually creating the required directory on Amazon Elastic File System using a AWS Lambda backed custom resource. While this worked, it introduced unnecessary complexity. Through experimentation, I discovered a more elegant solution—using EFS access points. In this post, I’ll walk through my journey, the challenges I faced, and how I ultimately simplified the setup with fewer resources and less maintenance.

Efficient collaboration and streamlined deployment processes are essential in modern development workflows, especially for teams working on complex projects. Feature branches and stack-based development approaches offer powerful ways to isolate changes, test effectively, and ensure seamless integration. However, proper strategies can make managing resources, dependencies, and environments challenging. This blog explores how to optimize feature branch workflows, maintain encapsulated logical stacks, and apply best practices like resource naming to improve clarity, scalability, and cost-effectiveness.

A CloudWatch alarm is triggered. Now what? I am not the first person to tell you that observability is essential to your cloud infrastructure. You are not done when you have set up CloudWatch alarms!

With modern clouds, you can build awesome things. You can bring your ideas to life within the hour, enabling experimentation and driving innovation. One of the significant advantages of the cloud is that you get a lot of security controls out of the box. But these security controls can also block you from being productive!

Relative Python imports can be tricky for lambda functions. I wrote a blog on this 3 years ago. But recently, I ran into the same issue with Dockerized lambda functions. So, I figured it was time for a new blog!

I like to combine technology with something more practical. This helps me understand the technology much better. Plus, when you have a practical example, it’s also easier to explain to my wife and friends. During the coronavirus pandemic, I started brewing beer. Brewing beer involves a lot of steps, and the brewer needs to take many steps. But some steps can be automated!

We all played around with AWS StepFunctions in our careers. It is a fantastic orchestration tool! But there are some scenarios where your cloud bill can explode in your face! In this blog, I will walk you through how you can end up in this situation and, maybe more importantly, how to avoid it!