Ecs

Overview There is a recurring pattern I keep seeing with side projects and small internal tools. The application is fine. The container is fine. The AWS part is what slows everything down. Not because ECS is bad. Quite the opposite: plain ECS gives you a lot of control. But that control comes with a lot of setup. Subnets, load balancer, listener rules, security groups, scaling policies, logging, IAM roles, health checks, certificates. For a production platform team this is normal life. For one container and one weekend project, it is a lot. ...

Building an ECS Cluster with Raspberry Pi 4 Nodes Using AWS ECS Anywhere In this guide, we’ll explore how to set up an Amazon Elastic Container Service (ECS) cluster using three Raspberry Pi 4 devices as external instances, leveraging AWS ECS Anywhere. We’ll delve into the capabilities of ECS, provide the necessary Terraform code to create the cluster, and walk through the steps to generate activation keys. Additionally, we’ll develop a Python 3 application that listens to an Amazon Simple Queue Service (SQS) queue for batch processing messages. This process involves downloading an object from Amazon S3, processing it through a mocked API, and storing the result in a DynamoDB table. We’ll also cover the Terraform configurations required to set up the DynamoDB table, SQS queue, S3 event notifications, and an Amazon Elastic Container Registry (ECR) repository for our Docker images. ...

Intro If you work with AWS, you might be involved in building infrastructure to enable some of your customers (both internal and external) to use a particular service, or just to try one of the hundreds open-source application available on Github. Furthermore, most of the ML/AI tools are shipped in docker containers and the philosophy -> if it runs on docker, it runs everywhere has been spread up to the highest level of management (nice, but… sometimes dangerous 😅 ed. ) And it’s pretty much true, buuuuut… ...

Intro If you work with AWS, you might be involved in building infrastructure to enable some of your customers (both internal and external) to use a particular service, or just to try one of the hundreds open-source application available on Github. Furthermore, most of the ML/AI tools are shipped in docker containers and the philosophy -> if it runs on docker, it runs everywhere has been spread up to the highest level of management (nice, but… sometimes dangerous 😅 ed. ) And it’s pretty much true, buuuuut… ...