AWS Lambda Getting Started
AWS Lambda is a serverless compute service that runs your code in response to events and automatically manages the underlying compute resources for you, making it easier to build applications that respond quickly to new information.
No matter whether you are new to AWS Lambda or you already have a use case in mind, choose your own path and follow the curated learning steps to get started on AWS Lambda.
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Path 1: Interactive Web- and API-based Microservices or ApplicationsUse AWS Lambda on its own or combined with other AWS services to build powerful web applications, microservices and APIs that help you to gain agility, reduce operational complexity, reduce cost and scale automatically.
Finally, you'll create a serverless web app with multiple microservices. You will host a static website, manage user authentication and build a serverless backend using AWS Amplify Console, Amazon Cognito, AWS Lambda, Amazon API Gateway, and Amazon DynamoDB.
This web reference architecture demonstrates how to use AWS Lambda in conjunction with other AWS services to build a serverless web app. This repository contains sample code for all the Lambda functions that make up the back end of the application.
Path 2: Data Processing ApplicationsServerless allows you to ingest, process and analyze high volumes of data quickly and efficiently. Learn how to build a scalable serverless data processing solution. Use Amazon Simple Storage Service ( Amazon S3) to trigger for data processing or load machine learning (ML) models from Amazon Elastic File System (EFS) to AWS Lambda to perform ML inference in real time.
Learn how to deploy machine learning models for real-time inference using AWS Lambda functions which can now mount an Amazon Elastic File System (EFS). With this, you can create a Lambda function that loads the Python packages and model from EFS, and performs the prediction based on a test event.
This Real-time File Processing reference architecture is a general-purpose, event-driven, parallel data processing architecture that uses AWS Lambda. This architecture is ideal for workloads that need more than one data derivative of an object.
Path 3: Real-Time Streaming Applications
Streaming data allows you to gather analytical insights and act upon them, but also presents a unique set of design and architectural challenges. Learn how to achieve several general goals of streaming data workloads by using AWS Lambda and Amazon Kinesis to capture the messages, to process and aggregate the records and finally to load the results into other downstream systems for analysis or further processing.
This reference architecture will use AWS Lambda and Amazon Kinesis to process real-time streaming data for application activity tracking, transaction order processing, click stream analysis, data cleansing, metrics generation, log filtering, indexing, social media analysis, and IoT device data telemetry and metering.
Path 4: No use case in mind? Start with AWS Lambda 101
New to AWS Lambda? Follow along the steps in this path, and build your first functional Lambda function with an event trigger.