Ingesting data into an OpenSearch cluster looks easy if you read the documentation. The truth is it is easy, but it all depends on how much you care about the data you are ingesting. Let me go one step back. Why do we even use OpenSearch? With the rise of AI, you also need a knowledge base. These knowledge bases can be hosted in OpenSearch. However, to use the OpenSearch database, you must also fill it out with data.

Have you ever been in a situation where you want to provision or configure things cross-stack? Splitting these into logical stacks is always good when dealing with more complex environments. I already shared this in one of my previous blogs. But this also introduces a different problem!

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 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 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 ECS Fargate, persistent storage is often a necessity. I initially attempted to solve this by manually creating the required directory on EFS using a 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 crucial 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.