apex VS Previous Serverless Version 0.5.x

Just skimmed through the generated one for go and seems like it's listing the package apex, which is archived (which usually means deprecated), so I'm not sure how useful the tool is!

Apex (label: contrib (easy)) Build, Deploy, and Manage AWS Lambda functions with ease

- With Lambda, you manage creating and building the container yourself, as well as updating the Lambda function code. There are tools out there such as sst or serverless.com which help streamline this.

If you'd like to use Lambda, usually you need to engineer FOR it, from day one, you don't (often) get to choose some other framework and shoehorn it into Lambda and Serverless. There's some great frameworks to help deploy code into Lambda easily and create REST endpoints for things, one such frameworks is serverless.com that helps easily deploy to it, but it lacks a framework for doing REST that also supports local emulation (as easily). For that, I recommend a framework by AWS called Chalice. This is an amazing REST framework that runs a proxy that works locally and deploys exactly the same on Lambda, it is Python however.

After knocking out a README with a set of goals and a list of TODOs to check off as I made progress, I spent about 10 hours over a weekend trying to get something to work. I used serverless for making Lambda easier, Github Actions for the deploy pipeline and store my credentials; and sadly I rolled my own access_token refresh logic because I couldn't find a helper that just did that for me! wtf!?

The backend is built with serverless.com (lambda, dynamodb, sqs, appsync). The good thing is that all the backend is stored in a file and you can deploy multiple stacks on the same account using seed.run . You don't really need EC2/Fargate when you have lambdas and you know that most of the time will be idle time. The same with cache I wouldn't think of it right now until you see the workload you are facing. Dynamodb once you understand it and have a proper design it's the fastest thing you can have. On my appsync calls I'm using Dynamodb as a cache because it's cheaper...

Apache Kafka allows for asynchronous communication in a distributed ecosystem. It allows producers to publish messages on topics that are then ingested by consumers interested in those topics. As a concept, pub-sub models have been around for ages. However, the beauty of Kafka is in the how — using partitions and consumer groups, Kafka can scale the rate of consumption of messages with minimal dev and economic overhead. In this tutorial, I’ll take you through how to provision a managed Kafka cluster using the AWS Managed Stream for Kafka (MSK) service. We’ll use the serverless framework to create and maintain the infrastructure for MSK and the supporting VPCs, subnets, etc.

I run my personal project on AWS. I has been running for 4+ years now and I never had a local environment. I took the serverless route. That is appsync, lambda, dynamodb, sqs to build the stack. I'm using serverless.com to have all the resources defined in a yaml files which will deploy multiple stacks. I'm using seed.run to manage that part because it's much more simple than to do it manually.

The solution is deployed using serverless.com

I believe the serverless framework has some built-in integration with it.

For your convenience I’ve made an example project on GitHub using Serverless Framework so you can try it for yourself.