Category: Amazon Simple Storage Service (S3)
In this article, you learn if it is possible to migrate a non-serverless web application to a serverless environment without changing much code. You learn different tools that can help you in this process, like the AWS Lambda Web Adaptor and AWS Amplify.
This blog post is written by Mark Richman, Senior Solutions Architect. Today, AWS is launching a new capability to integrate the Amazon CodeWhisperer experience with the AWS Lambda console code editor. Amazon CodeWhisperer is a machine learning (ML)–powered service that helps improve developer productivity. It generates code recommendations based on their code comments written in […]
This post is written by Doug Toppin, Software Development Engineer, and Kishore Dhamodaran, Solutions Architect. In public speaking, filler phrases can distract the audience and reduce the value and impact of what you are telling them. Reviewing recordings of presentations can be helpful to determine whether presenters are using filler phrases. Instead of manually reviewing […]
This post expands on the functionality introduced with the PowerShell custom runtime for AWS Lambda. The previous blog explains how the custom runtime approach makes it easier to run Lambda functions written in PowerShell. You can add additional functionality to your PowerShell serverless applications by importing PowerShell modules, which are shareable packages of code. Build your own […]
This blog was written by Monica Cortes Sack, Solutions Architect, Oskar Neumann, Partner Solutions Architect, and Dhiraj Mahapatro, Principal Specialist SA, Serverless. AWS Step Functions now support over 220 services and over 10,000 AWS API actions. This enables you to use the AWS SDK integration directly instead of writing an AWS Lambda function as a proxy. One […]
This post is written by Thomas Moore, Solutions Architect, Serverless. When using AWS Lambda to build serverless applications, customers often need to retrieve parameters from an external source at runtime. This allows you to share parameter values across multiple functions or microservices, providing a single source of truth for updates. A common example is retrieving […]
This post is written by Veda Raman, SA Serverless, Casey Gerena, Sr Lab Engineer, Dan Fox, Principal Serverless SA. For real-time machine learning inferencing, customers often have several machine learning models trained for specific use-cases. For each inference request, the model must be chosen dynamically based on the input parameters. This blog post walks through the architecture […]
This blog post shows how to create a scalable difference checking tool for objects stored in S3 buckets. The Lambda function is invoked when S3 writes new versions of an object to the bucket. This example also shows how to remove earlier versions of object and define a set number of versions to retain.
It’s best practice to store the output of the Lambda function in a different bucket or AWS resource than the source bucket. In cases where you need to store the processed object in the same bucket, I show three different designs to help minimize the risk of recursive invocations.
A serverless face blurring service can provide a simpler way to process photos in workloads with large amounts of traffic. This post introduces an example application that blurs faces when images are saved in an S3 bucket. The S3 PutObject event invokes a Lambda function that uses Amazon Rekognition to detect faces and GraphicsMagick to process the images.