Lab Overview
Amazon API Gateway offers several native authorization mechanisms, such as managed API keys, IAM Roles, and custom authorizers. API Keys (with the help of Usage Plans) can help manage custom throttling and quotas for your API consumers. IAM is a good choice when your consumers require access to AWS resources and you need to manage permissions on a per-user basis. However, custom authorizers give you much more flexibility. With custom authorizers, you can implement any 3rd-party integration and generate very granular authorization policies.
In this Lab, we will learn how to implement a custom authorizer in AWS Lambda to secure your API Gateway Resources.
Lab Objectives
Upon completion of this Lab you will be able to:
- Understand API Gateway request authorization
- Explain the advantages of using custom authorizers in API Gateway
- Create Lambda functions to implement custom authorizers using AWS Lambda blueprints
- Test custom authorizers using methods appropriate at each stage of deployment
Lab Prerequisites
You should be familiar with:
- AWS Lambda basics
- API Gateway basics
The following content can be used to fulfill the prerequisites:
Updates
December 1st, 2022 - Updated the instructions and screenshots to reflect the latest UI
May 12th, 2022 - Updated lab to utilize a Cloud Academy-hosted Linux command-line interface
November 17th, 2021 - Fixed the link to the lambda function complete code listing
January 22nd, 2021 - Updated AWS Lambda lab steps to reflect the latest user interface updates
January 10th, 2019 - Added a validation Lab Step to check the work you perform in the Lab
Nov. 15th, 2018 - Lab completely updated including easier-to-follow instructions, screenshots to match the latest experience, and removal of security warnings in the AWS Console.
Alex is an Italian Software Engineer with a great passion for web technologies and music.
He spent the last 5 years building web products and deepening his knowledge on full-stack web development and software design, with a main focus on frontend and UX.
Despite being a passionate coder, Alex worked hard on his software and sound engineering background, which provides him the tools to deal with multimedia, signal processing, machine learning, AI, and many interesting topics related to math and data science.
Indeed, he had the opportunity to study and live in a very young and motivating environment in Bologna and Milan, two of the biggest and oldest Italian Universities. These experiences lead him to work on several projects involving robotics, machine intelligence, music semantic analysis and modern web development.