Category: Analytics

In this post, I show how the existing settings in the Alleycat application are not sufficient for handling the expected amount of traffic. I walk through the metrics visualizations for Kinesis Data Streams, Lambda, and DynamoDB to find which quotas should be increased.

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.

Using resources such as NAT Gateways and VPC endpoints with PrivateLink, you can ensure that your data remains secure while also granting access to resources such as Lambda to help you create a Kafka consumer application. This post provides some tips to help you set up a Lambda function using Kafka as a trigger. It also explains various options available to send data securely.

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.

This post is written by Shashi Shankar, Application Architect, Shared Delivery Teams Freshdesk is an omnichannel customer service platform by Freshworks. It provides automation services to help speed up customer support processes. The Freshworks connector to Amazon EventBridge allows real time streaming of Freshdesk events with minimal configuration and setup. This integration provides real-time insights […]

In this post, we show how SNS delivery to Kinesis Data Firehose enables you to integrate SNS with storage and analytics services. The example shows how to create an SNS subscription to use a Kinesis Data Firehose delivery stream to store SNS messages in an S3 bucket.

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.