AWS Lambda Getting Started
Choose your own path
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.
Path 1: Interactive Web and API-based Microservices or Applications
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Use 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.
Step 1: Get Started with Lambda HTTP
Learn how to build a dynamic web page from a single Lambda function. You will start off by assigning HTTPS endpoints to your Lambda function, which uses a Lambda Function URL to call your function directly without having to learn, configure and operate additional services. This is ideal for single-function microservices. Learn more
Step 2: Use Lambda with Amazon API Gateway
Next, you will use Amazon API Gateway to create a REST API and a resource (Amazon DynamoDB). When you call the API through an HTTPS endpoint, API Gateway invokes the Lambda function. This is ideal for microservices with multiple functions, leveraging Amazon API Gateway to map each function to API endpoints, methods and resources. Learn more
Step 3: Build a Basic Web Application
Now you are ready to create a simple web application using AWS Lambda, Amazon API Gateway, Amazon DynamoDB, and AWS Amplify Console. You will first build a static web app that renders "Hello World." Then you will learn how to add functionality to the web app so the text that displays is based on a custom input you provide. Learn more
Step 4: Build a Multi-Microservice Web Application
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. Learn more
Reference Architecture
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. Learn more
Path 2: Data Processing Applications
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Serverless 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.
Step 1: Use an Amazon S3 trigger to create thumbnail images
Start off by creating a Lambda function and configure a trigger for Amazon S3. For each image file uploaded to an S3 bucket, Amazon S3 invokes a function which reads the image object from the source S3 bucket and creates a thumbnail image to save in a target S3 bucket. Learn more
Step 2: Large-Scale Parallel Data Processing
Also, learn how to orchestrate large-scale parallel workloads which convert .mp4 and .mov files from S3 into multiple Gif animations for timeline scrubbing. With distributed map from AWS Step Functions, jobs scale up quickly invoking thousands of parallel Lambda functions to complete jobs faster. Learn more
Step 3: Serverless Image Processing Hands-on Workshop
Next, you will learn how to build an image processing workflow in response to an image uploaded to Amazon S3 using a simple, powerful, fully managed service AWS Step Functions together with AWS Lambda, Amazon DynamoDB, and Amazon Simple Notification Service (SNS). Learn more
Step 4: Building Scalable Data Processing Applications
In this blog series, learn more on how to design and deploy serverless applications designed around the Amazon S3-to-AWS Lambda architecture patterns. The solutions presented use AWS services to create scalable serverless architectures, using minimal custom code. Learn more
Step 5: Pay as You Go Machine Learning Inference with AWS Lambda
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. Learn more
Reference Architecture
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. Learn more
Path 3: Real-Time Streaming Applications
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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.
Step 1: Use AWS Lambda with Amazon Kinesis
Amazon Kinesis is a service that makes it easy to collect, process and analyze video and data streams in real-time. You will start off by creating a Lambda function to consume events from a Kinesis stream. Learn more
Step 2: Stream Data Processing Hands-on Workshop
Next, you will build a comprehensive serverless data processing application to process real-time data streams using Amazon Kinesis to create data streams and AWS Lambda to process streams in real-time. Learn more
Step 3: Building Serverless Applications with Streaming Data
Finally, read this blog series to learn how to build a streaming data backend for a home fitness system by using a serverless approach. You will learn key streaming concepts and how to handle these in a serverless workload. Learn more
Reference Architecture
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. Learn more
Path 4: No use case in mind? Start with AWS Lambda 101
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New to AWS Lambda? Follow along the steps in this path, and build your first functional Lambda function with an event trigger.
Step 1: Log into Your AWS Account
First, log into the AWS Management Console and set up your root account. With the AWS Free Tier, you get 1 million free requests per month.
Step 2: Your First Lambda Function
Next, you will be ready to create and deploy a simple serverless Hello World function using the Lambda console, and review your output metrics. Learn more
Step 3: Set Up Triggers for Lambda
Finally, set up an event trigger for Amazon S3 that will invoke your Lambda function when an event occurs. Learn more