Category: Serverless

In this post, you’ll learn how to process Amazon S3 objects at scale with the new AWS Step Functions Distributed Map S3 prefix and transformation capabilities.

You can use AWS Step Functions to orchestrate complex business problems. However, as workflows grow and evolve, you can find yourself grappling with monolithic state machines that become increasingly difficult to maintain and update. In this post, we show you strategies for decomposing large Step Functions workflows into modular, maintainable components.

In this post, you learn how to implement blue/green deployments by using Amazon API Gateway for your APIs. For this post, we use AWS Lambda functions on the backend. However, you can follow the same strategy for other backend implementations of the APIs. All the required infrastructure is deployed by using AWS Serverless Application Model (AWS SAM).

Welcome to the 30th edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. At the end of a quarter, we share the most recent product launches, feature enhancements, blog posts, videos, live streams, and other interesting things that you might have missed! In case you missed our last ICYMI, check out […]

Users usually package their function code as container images when using machine learning (ML) models that are larger than 250 MB, which is the Lambda deployment package size limit for zip files. In this post, we demonstrate an approach that downloads ML models directly from Amazon S3 into your function’s memory so that you can continue packaging your function code using zip files.

In this post, we show how to use Step Functions Distributed Map to process Amazon S3 objects and export results to Amazon S3 Tables, creating a scalable and maintainable data processing pipeline.

This post demonstrates how you can connect CloudFront with a Private REST API in Amazon REST API Gateway using a VPC origin.

This post presents architectural best practices for building resilient serverless applications, demonstrated through a multi-Region serverless authorizer implementation.

This post explores two complementary approaches for non-real-time scenarios: buffered asynchronous processing for time-intensive individual requests, and batch processing for scheduled or event-driven workflows.

This two-part series explores the different architectural patterns, best practices, code implementations, and design considerations essential for successfully integrating generative AI solutions into both new and existing applications. In this post, we focus on patterns applicable for architecting real-time generative AI applications.