Category: HAQM Kinesis

These organizations use third-party satellite-powered terminal devices for remote monitoring using telemetry and NMEA-0183 formatted messages generated in near real time. This post demonstrates how to implement a satellite-based remote alerting and response solution on the AWS Cloud to provide time-critical alerts and actionable insights, with a focus on telemetry message ingestion and alerts. Key services in the solution include HAQM API Gateway, HAQM Data Firehose, and HAQM Location Service.

In this post, we show how to use HAQM Kinesis Data Streams to buffer and aggregate real-time streaming data for delivery into HAQM OpenSearch Service domains and collections using HAQM OpenSearch Ingestion. You can use this approach for a variety of use cases, from real-time log analytics to integrating application messaging data for real-time search. In this post, we focus on the use case for centralizing log aggregation for an organization that has a compliance need to archive and retain its log data.

We are excited to launch Kinesis Client Library 3.0, which enables you to reduce your stream processing cost by up to 33% compared to previous KCL versions. KCL 3.0 achieves this with a new load balancing algorithm that continuously monitors the resource utilization of workers and redistributes the load evenly to all workers. In this post, we discuss load balancing challenges in stream processing using a sample workload, demonstrating how uneven load distribution across workers increases processing costs.

In this post, we discuss how you can send real-time data streams into Iceberg tables on HAQM S3 by using HAQM Data Firehose. HAQM Data Firehose simplifies the process of streaming data by allowing users to configure a delivery stream, select a data source, and set Iceberg tables as the destination. Once set up, the Firehose stream is ready to deliver data.

HAQM Kinesis Data Analytics for SQL is a data stream processing engine that helps you run your own SQL code against streaming sources to perform time series analytics, feed real-time dashboards, and create real-time metrics. AWS has made the decision to discontinue Kinesis Data Analytics for SQL, effective January 27, 2026. In this post, we explain why we plan to end support for Kinesis Data Analytics for SQL, alternative AWS offerings, and how to migrate your SQL queries and workloads.

This post demonstrates how to implement a dynamic rules engine using HAQM Managed Service for Apache Flink. Our implementation provides the ability to create dynamic rules that can be created and updated without the need to change or redeploy the underlying code or implementation of the rules engine itself. We discuss the architecture, the key services of the implementation, some implementation details that you can use to build your own rules engine, and an AWS Cloud Development Kit (AWS CDK) project to deploy this in your own account.

In this, we will provide a step-by-step guide showing you how you can build a real-time OLAP datastore on HAQM Web Services (AWS) using Apache Pinot on HAQM Elastic Compute Cloud (HAQM EC2) and do near real-time visualization using Tableau. You can use Apache Pinot for batch processing use cases as well but, in this post, we will focus on a near real-time analytics use case.

This post is co-written with Çağrı Çakır and Özge Kavalcı from PostNL. PostNL is the designated universal postal service provider for the Netherlands and has three main business units offering postal delivery, parcel delivery, and logistics solutions for ecommerce and cross-border solutions. With 5,800 retail points, 11,000 mailboxes, and over 900 automated parcel lockers, the […]

Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and HAQM Kinesis Data Streams for processing streaming data and HAQM Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.

This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.