If you're venturing into the world of cloud computing, you've likely encountered the term serve. But what does it really mean? Imagine building applications without the hassle of managing servers—no more worrying about provisioning, scaling, or maintaining infrastructure. Enter AWS Lambda, Amazon's serverles compute service that lets you run code in response to events without provisioning or managing servers.
In this guide, we'll explore how to leverage AWS Lambda to build scalable applications, share best practices, and provide real-world examples to help you get started.
AWS Lambda is a compute service that automatically runs your code in response to events such as changes in data or system state. You upload your code, and Lambda takes care of everything required to run and scale the execution to meet demand. This means you can focus solely on your application's logic without worrying about the underlying infrastructure.
Lambda automatically scales your application by running code in response to each trigger. This makes it effortless to build highly scalable applications that can handle massive workloads without a server fleet.
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Since AWS manages the infrastructure, you don't have to worry about server maintenance, patching, or scaling. This allows you to focus more on developing features and less on operational
User Uploads Image: The user uploads an image to an S3 bucket.
Trigger Lambda Function: The S3 bucket is configured to trigger a Lambda function whenever a new object is created.
Process Image: The Lambda function resizes the image using a library like Pillow (Python) or Sharp (Node.js).
Store Processed Image: The resized image is stored back into the S3 bucket.
This workflow is entirely AWS Lambda handles the compute, and S3 handles the storage. As the number of image uploads increases, Lambda automatically scales to handle the load.
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