Category: Advanced (300)

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.

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).

When migrating Amazon EC2 instances with encrypted Amazon EBS volumes across AWS Regions with in the same account or a different account, you face a particular challenge: AWS KMS keys are AWS Region-specific and cannot be shared across AWS Regions. This post provides a step-by-step approach to successfully migrate your encrypted EC2 instances without compromising your security posture by sharing your KMS keys.

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.

When expanding your Graviton deployment across multiple AWS Regions, careful planning helps you navigate considerations around regional instance type availability and capacity optimization. This post shows how to implement advanced configuration strategies for Graviton-enabled EC2 Auto Scaling groups across multiple Regions, helping you maximize instance availability, reduce costs, and maintain consistent application performance even in AWS Regions with limited Graviton instance type availability.