Category: Kinesis Data Streams

In this post, I explain the all-time leaderboard logic in the Alleycat application. This is an asynchronous, eventually consistent process that checks batching of incoming records for new personal records. This uses Kinesis Data Firehose to provide a zero-administration way to deliver and process large batches of records continuously.

In this post, I explain the all-time leaderboard logic in the Alleycat application. This is an asynchronous, eventually consistent process that checks batching of incoming records for new personal records. This uses Kinesis Data Firehose to provide a zero-administration way to deliver and process large batches of records continuously.

This post focuses on ingesting data into Kinesis Data Streams. I explain the two approaches used by the Alleycat frontend and the simulator application and highlight other approaches that you can use. I show how messages are routed to shards using partition keys. Finally, I explore additional factors to consider when ingesting data, to improve efficiency and reduce cost.

In this post, I introduce the Alleycat racing application for processing streaming data. I explain the virtual racing logic and provide an overview of the application architecture. I summarize the deployment process for the different parts of the solution and show how to test the frontend once the deployment is complete.

The default behavior for stream processing in Lambda functions enables entire batches of messages to succeed or fail. You can also use batch bisecting functionality to retry batches iteratively if a single message fails. Now with custom checkpoints, you have more control over handling failed messages.

With tumbling windows, you can calculate aggregate values in near-real time for Kinesis data streams and DynamoDB streams. Unlike existing stream-based invocations, state can be passed forward by Lambda invocations. This makes it easier to calculate sums, averages, and counts on values across multiple batches of data.

Over the last year, I have been talking about “storage first” serverless patterns. With these patterns, data is stored persistently before any business logic is applied. The advantage of this pattern is increased application resiliency. By persisting the data before processing, the original data is still available, if or when errors occur. Common pattern for […]

Welcome to the 10th edition of the AWS Serverless ICYMI (in case you missed it) quarterly recap. Every quarter, we share all of the most recent product launches, feature enhancements, blog posts, webinars, Twitch live streams, and other interesting things that you might have missed! In case you missed our last ICYMI, checkout what happened […]