Category: Advanced (300)

Event-driven applications often need to process data in real-time. When you use AWS Lambda to process records from Apache Kafka topics, you frequently encounter two typical requirements: you need to process very high volumes of records in close to real-time, and you want your consumers to have the ability to scale rapidly to handle traffic spikes. Achieving both necessitates understanding how Lambda consumes Kafka streams, where the potential bottlenecks are, and how to optimize configurations for high throughput and best performance.

Modern generative AI applications often need to stream large language model (LLM) outputs to users in real-time. Instead of waiting for a complete response, streaming delivers partial results as they become available, which significantly improves the user experience for chat interfaces and long-running AI tasks. This post compares three serverless approaches to handle Amazon Bedrock LLM streaming on Amazon Web Services (AWS), which helps you choose the best fit for your application.

Today, AWS is announcing tenant isolation for AWS Lambda, enabling you to process function invocations in separate execution environments for each end-user or tenant invoking your Lambda function. This capability simplifies building secure multi-tenant SaaS applications by managing tenant-level compute environment isolation and request routing, allowing you to focus on core business logic rather than implementing tenant-aware compute environment isolation.

Today, AWS announced support for response streaming in Amazon API Gateway to significantly improve the responsiveness of your REST APIs by progressively streaming response payloads back to the client. With this new capability, you can use streamed responses to enhance user experience when building LLM-driven applications (such as AI agents and chatbots), improve time-to-first-byte (TTFB) performance for web and mobile applications, stream large files, and perform long-running operations while reporting incremental progress using protocols such as server-sent events (SSE).

In this post, we explore using AWS Step Function state machine with asynchronous AWS services, look at some scenarios where the processing time can be unpredictable, explain when traditional solutions such as polling (periodically check) fall short, and demonstrate how to implement a generalized callback pattern to handle asynchronous operations into a more manageable synchronous flow.

This post examines the benefits of transitioning Lambda functions to IPv6, provides practical guidance for implementing dual-stack support in your Lambda environment, and considerations for maintaining compatibility with existing systems during migration.

In this post, you’ll learn how to use AWS Step Functions Distributed Map to process Amazon Athena data manifest and Parquet files through a step-by-step demonstration.

In this post, we explore how to optimize processing array data embedded within complex JSON structures using AWS Step Functions Distributed Map. You’ll learn how to use ItemsPointer to reduce the complexity of your state machine definitions, create more flexible workflow designs, and streamline your data processing pipelines—all without writing additional transformation code or AWS Lambda functions.

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).