aws-lambda-java-libs VS serverless-application-model

AWS Lambda is a service that runs your code without you managing any servers. You write your code, deploy it to Lambda, and it takes care of the infrastructure — servers, networking, security, and scaling.

Clay can replace the Lambda and API chain if you'd rather avoid custom code. You set up a Clay table as the enrichment layer, trigger it from Segment via webhook, and it handles the waterfall and CRM push without writing a function. The tradeoff: less control over scoring logic and higher cost per enriched contact.

To show why this matters, take a look at the following example. I have three AWS Lambda functions, Lambda being the serverless compute service, that each handle a different endpoint on the same API. But, almost everything about them is the same. They have the same runtime, the same memory configuration, and nearly the same structure. The only differences are the name, handler, and possibly some environment variables.

Query Expansion and Decomposition: Amazon Bedrock query expansion broadens search; AWS Lambda query decomposition breaks complex queries into sub-queries; AWS Step Functions orchestrates multi-step retrieval.

You need to understand synchronous and asynchronous inference patterns, event-driven architectures using Amazon EventBridge, workflow orchestration with AWS Step Functions, data processing with AWS Lambda, state management with Amazon DynamoDB, and security with AWS Identity and Access Management (IAM). The exam tests your ability to design serverless architectures that scale automatically, handle failures gracefully, and optimize costs.

Assumes familiarity with AWS CDK, AWS Lambda, and WhatsApp/Instagram API concepts. Deployment takes approximately 15 minutes per stack.

Until recently, GraphQL response streaming with AWS Lambda was only possible using Lambda Function URLs. But AWS now supports response streaming with Amazon API Gateway, and graphql-yoga has added support for this feature. This opens up new possibilities for building responsive GraphQL APIs with the full feature set of API Gateway (custom domains, usage plans, API keys, etc.).

In this application, we will create products and retrieve them by their ID and use Amazon DynamoDB as a NoSQL database for the persistence layer. We use Amazon API Gateway, which makes it easy for developers to create, publish, maintain, monitor, and secure APIs. Of course, we rely on AWS Lambda to execute code without the need to provision or manage servers. We also use AWS SAM, which provides a short syntax optimised for defining infrastructure as code (hereafter IaC) for serverless applications. For this article, I assume a basic understanding of the mentioned AWS services, serverless architectures on AWS, and AWS SAM.

You can implement automated solutions using services such as Amazon EventBridge and AWS Lambda functions to detect and remediate drift automatically. This approach monitors for drift events and triggers remediation workflows without manual intervention.

Nowadays, many Cloud implementations will make use of serverless architectures, such as AWS Lambdas and API Gateways to implement micro-services, or other similar functionality to deliver business logic without the need to manage servers.

AWS SAM CLI, the command-line tool for building and deploying serverless applications, now supports AWS CloudFormation Language Extensions. The one I am most excited about is Fn::ForEach, which brings dynamic looping to your YAML templates, but it's close. If you, like me, have been copy-pasting resource definitions to infinity, that stops today.

A simple Google search found it: a tool called SAM — Serverless Application Model — something that could read the YAML, package the application, and deploy the whole thing: function, gateway, code, all of it 📦🚀.

Move to CDK or SAM.

In this case, I used the AWS Lambda Web Adapter and SAM (Serverless Application Model) to deploy a simple server application built with the Go web framework Echo via Lambda Function URLs.

In this post, I will share WebRTC.ventures' best practices in automating the deployment of AI-powered voice assistants for Amazon Connect, moving beyond manual, click-by-click setups to a robust, scalable Infrastructure as Code (IaC) approach. We’ll explore how to manage both static and dynamic resources, leverage tools like Terraform and AWS Serverless Application Model (SAM), and even set up an automated deployment pipeline using Github Actions to deploy your conversational IVR solutions efficiently.

My first stop was AWS SAM (Serverless Application Model), which is AWS's official tool for local development. With SAM, you can invoke a Lambda function on your machine using sam local invoke, or even spin up a local API Gateway using sam local start-api. This means you can test API calls before deploying to AWS, which is super useful.

To expose your FastAPI endpoints using AWS API Gateway, you will use AWS Serverless Application Model (AWS SAM). AWS SAM simplifies the process of building and deploying serverless applications on AWS by providing a simplified syntax for defining AWS resources such as Lambda functions, API Gateway, IAM roles, and other related services, all within a template.yaml file.

AWS SAM GitHub Repository