Become an AWS pro by learning to run serverless applications with AWS Lambda. Developed by AWS Solution Certified Architects. Get hands-on, no setup, no cleanup, no hassle.

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In the world of modern development, serverless computing has become table stakes. Serverless computing has seen a major uptake with AWS Lambda having an increased adoption of 667% in 2018. Companies are making the switch to serverless for shorter time to market and decreased operational costs, but for you the advantage lies in the ability to offload the burden of managing infrastructure to serverless platforms, so you can focus on building even better apps.

In this course, you will learn how to run serverless applications using AWS Lambda. You’ll start with the basics such as creating a web service where you’ll learn the steps to deploy an AWS Serverless Application Model (SAM). You’ll then move on to more advanced topics such as handling HTTP Requests, using external storage, and managing sessions and user workflows. By the end of this course, you will be ready to work with AWS Lambda in a professional setting, and you’ll have a great, transferable skill that employers will love to see.

Running Serverless Applications with AWS Lambda

In order to complete the function, you'll need to tell it which bucket to use (line 8 from the code in the previous lesson). Lambda functions don't really know about SAM and CloudFormation resources, so the function can't just ask for the `UploadS3Bucket` resource. SAM will create the bucket using a randomised name, and you need to tell the Lambda function about the actual value. 

You definitely don't want to hard code a bucket name in the function source, because then SAM can't automatically manage buckets. You could change the API Gateway resource to stick that information onto the request while it's passing through the API, for example as an additional header, but that requires messy request transformations. For situations such as this one, it's best to set a Lambda environment variable. 

## Environment variables 
_Environment variables_ are textual key-value pairs assigned to a running process. With a Node.js Lambda runtime, you can read them using the standard Node `process.env` object. SAM lets you configure those values in the template, so it's easy to pass the actual bucket name to a function based on the logical bucket reference. 

Environment variables are great for configuring Lambda functions with references to other resources in the same SAM template. For more complex configuration scenarios, such as rotating secrets and reconfiguring services without redeployment, check out the [AWS Systems Manager Parameter Store](https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-paramstore.html) and [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/). 

It helps to use all uppercase letters for environment variable names to clearly differentiate them from other values, but you can use any naming convention you like. The `process-form.js` file is updated to look like in the following listing:



In order to complete the function, you'll need to tell it which bucket to use (line 8 from the code in the previous lesson). Lambda functions don't really know about SAM and CloudFormation resources, so the function can't just ask for the `UploadS3Bucket` resource. SAM will create the bucket using a randomised name, and you need to tell the Lambda function about the actual value. 

You definitely don't want to hard code a bucket name in the function source, because then SAM can't automatically manage buckets. You could change the API Gateway resource to stick that information onto the request while it's passing through the API, for example as an additional header, but that requires messy request transformations. For situations such as this one, it's best to set a Lambda environment variable. 

# Environment variables 
_Environment variables_ are textual key-value pairs assigned to a running process. With a Node.js Lambda runtime, you can read them using the standard Node `process.env` object. SAM lets you configure those values in the template, so it's easy to pass the actual bucket name to a function based on the logical bucket reference. 

Environment variables are great for configuring Lambda functions with references to other resources in the same SAM template. For more complex configuration scenarios, such as rotating secrets and reconfiguring services without redeployment, check out the [AWS Systems Manager Parameter Store](https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-paramstore.html) and [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/). 

It helps to use all uppercase letters for environment variable names to clearly differentiate them from other values, but you can use any naming convention you like. The `process-form.js` file is updated to look like in the following listing:



In this lesson, you will learn how to let SAM configure the environment variables​ for your application. 

Passing Resource References to Functions

Getting Started

Setup Tools for Local Development

Creating a Web Service

Host a Serverless Application on AWS Lambda

Development and Troubleshooting

Core Features of AWS Lambda

Handling HTTP Requests

Using External Storage

Cheaper, Faster, Serverless

Handling Platform Events

Using Application Components

Managing Sessions and User Workflows

Designing Robust Applications

Deployment Options

Process Amazon SNS Notifications with AWS Lambda

Passing Resource References to Functions