aws-cdk VS amazon-sqs-java-messaging-lib

https://github.com/aws/aws-cdk#at-a-glance is the "generate cloudformation using code," and is the AWS version of troposphere as best I can tell

environment: Specifies the environment variable that will be available to our Lambda function. In this case I wrote an environment for RDS Database. Anyway, later I will write about AWS CDK using Python for creating database clusters, etc.

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

For KMS 🔑, Each key is ~$1/mo, and with CDK, keys are generated on a massive scale, if not centralized.

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.

Save to SQS (i.e. save each Event to Amazon’s SQS queuing service)

Amazon SQS

Messaging/Queueing Systems (Amazon SQS, RabbitMQ, Beanstalkd)

The last stage is productionizing the model. The goal of this phase is to create a system to process each image/video, gather the relevant features and inputs to the models, integrate the models into a hosting service, and relay the corresponding model predictions to downstream consumers like the MCF system. We used an existing Safety service, Content Classification Service, to implement the aforementioned system and added two specialized queues for our processing and various service integrations. To use the model for online, synchronous inference, we added it to Gazette, Reddit’s internal ML inference service. Once all the components were up and running, our final step was to run A/B tests on Reddit to understand the live impact on areas like user engagement before finalizing the entire detection system.