aws-lambda-java-libs VS deploy-cloud-functions

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.

If this sounds like Cloud Functions, here's the history. Cloud Functions 1st gen ran on older, separate infrastructure with strict limits: 9-minute timeouts, one request per instance, no concurrency. Cloud Functions 2nd gen (GA in 2022) was already built on top of Cloud Run under the hood, which unlocked 60-minute timeouts and multi-request concurrency. In 2024, Google made it official and rebranded 2nd gen as Cloud Run functions, consolidating everything under the Cloud Run name. So this isn't a new product. It's the recognition that the infrastructure was already unified. If your functions outgrow the one-entry-point-per-deployment model and you need routing, middleware, or multiple endpoints behind a single URL, you swap it for a full service on the same platform. No migration, no new infrastructure.

Cloud Functions (GCF) -- originally serverless functions to compete with AWS Lambda; latest generation rebranded as Cloud Run Functions

Of course, I can't just directly give my static website permissions to modify my databases, which is why I created a Cloud Function as a "middle-man" -- we should always assume there will be malicious actors that will cause irreparable damage if they have direct access to a database (I don't want to get charged by Google Cloud hehe).

It’s a lightweight GitHub App built with Probot and deployed serverlessly on GCF. Here's what it does:

Backend: It handles API functions (e.g. AWS Lambda, Cloudflare Workers, GCP Functions)

Serverless architectures are revolutionizing software development by removing the need for server management. Cloud services like AWS Lambda, Google Cloud Functions, and Azure Functions allow developers to concentrate on writing code, as these platforms handle scaling automatically.

Google Cloud Functions bases pricing on invocations, runtime, and memory with competitive free tier options

Google Cloud Functions Google Cloud Functions is a scalable serverless execution environment for building and connecting cloud services. It provides triggers automatically, with out-of-the-box support for HTTP and event-driven triggers from GCP services. There are two types of Google Cloud Functions: API cloud functions and event-driven cloud functions. The API cloud functions are invoked from standard HTTP requests, while the events-driven cloud functions handle events from the cloud infrastructure. The event driver provides you with a trigger, which is stored in the data, and then sends you a response according to your function.