aws-cdk VS aws-graviton-getting-started

Today, I will show you how to build Amazon Location Service, which allows you to build location-based applications within your AWS environment using AWS Cloud Development Kit (AWS CDK) and AWS CloudFormation. I will also show examples of the recently popular CDK Migrate and AWS CloudFormation IaC generator.

AWS Cloud Development Kit (CDK): is an open-source software development framework to define your cloud application resources using familiar programming languages.

I am not one to build programming languages on a whim. In fact, I've spent the last five years building the AWS CDK, which is a multi-language library that addresses some of the challenges I am talking about by allowing developers to define cloud infrastructure using their favorite programming language.

I recently started exploring SST as an alternative to my favorite full-stack set consisting of Projen, AWS CDK, and React. I have been thoroughly impressed with the experience so far. In this article, I will demonstrate how to create a Next.js App Router S3 Picture Uploader using SST.

AWS CDK

Here is a github repository containing the code and instructions on how to automate this whole setup using AWS CDK.

Now that you have a working smart contract event listener, we'll deploy the resources to AWS using AWS CDK, which is an Infrastructure as Code (IaC) tool. AWS CDK allows you to configure, deploy, and manage AWS cloud resources using popular programming languages such as TypeScript.

Amazon CDK is an excellent example of an internal tool (which they’ve since open-sourced) for defining resources for your cloud application using well-known programming languages.

This is actually already available but is still an experimental feature so there are no guarantees about the outcome or stability of the functionality.

Interestingly, AWS CDK and SAM are both explicitly mentioned. While CDK broadly addresses Infrastructure as Code, SAM is highlighted for its role in developing serverless data pipelines - a hugely underrated concept.

The incredible Syl Taylor just released two new workshops related to AWS Graviton (the processors developed by the company with the best price/performance combination) and Machine Learning:

AWS: Graviton-powered instances (Arm64 processor architecture)

Use Graviton V2, Spot & Burstable instances and check the data in AWS Cost Explorer to find the right size for your instances

Use Graviton V2, Spot & Burstable instances and check the data in AWS Cost Explorer to find the right size for your instances

If you have applications using zlib make sure to check alternative versions of the library. Cloudflare zlib is a good one, and there may be others available. Watch the AWS Graviton Getting Started for the latest information.

AWS manufactures their own - https://aws.amazon.com/ec2/graviton/

In other Services such as EC2, EKS, ECS you can provision Instance type that support Graviton2. You can see more details about supported instance here

Establishing short and long-term sustainability goals for your workload, such as reducing the computed resources required per transaction or improving the power efficiency of your compute workload, reducing the network data traveled per request. For example, even if you are not able to go serverless with your application in the first step, you can start to decouple parts of your workload and create small functions to get there in the long run. Other options would include containerizing your workloads or improving the energy efficiency of your compute load by moving to Graviton2-based instances and/or functions. There are multiple transformation guides for transitioning workloads to AWS Graviton2 (EC2, Lambda, RDS & Aurora ). Here is also a nice list of AWS services where graviton processors are available.