AWS Big Data Blog
Category: HAQM Kinesis
PackScan: Building real-time sort center analytics with AWS Services
In this post, we explore how PackScan uses HAQM cloud-based services to drive real-time visibility, improve logistics efficiency, and support the seamless movement of packages across HAQM’s Middle Mile network.
How Airties achieved scalability and cost-efficiency by moving from Kafka to HAQM Kinesis Data Streams
Airties is a wireless networking company that provides AI-driven solutions for enhancing home connectivity. This post explores the strategies the Airties team employed during their migration from Apache Kafka to HAQM Kinesis Data Streams, the challenges they overcame, and how they achieved a more efficient, scalable, and maintenance-free streaming infrastructure.
Unify streaming and analytical data with HAQM Data Firehose and HAQM SageMaker Lakehouse
In this post, we show you how to create Iceberg tables in HAQM SageMaker Unified Studio and stream data to these tables using Firehose. With this integration, data engineers, analysts, and data scientists can seamlessly collaborate and build end-to-end analytics and ML workflows using SageMaker Unified Studio, removing traditional silos and accelerating the journey from data ingestion to production ML models.
Announcing end-of-support for HAQM Kinesis Client Library 1.x and HAQM Kinesis Producer Library 0.x effective January 30, 2026
HAQM Kinesis Client Library (KCL) 1.x and HAQM Kinesis Producer Library (KPL) 0.x will reach end-of-support on January 30, 2026. Accordingly, these versions will enter maintenance mode on April 17, 2025. During maintenance mode, AWS will provide updates only for critical bug fixes and security issues. Major versions in maintenance mode will not receive updates for new features or feature enhancements.
Deploy real-time analytics with StarTree for managed Apache Pinot on AWS
In this post, we introduce StarTree as a managed solution on AWS for teams seeking the advantages of Pinot. We highlight the key distinctions between open-source Pinot and StarTree, and provide valuable insights for organizations considering a more streamlined approach to their real-time analytics infrastructure.
Governing streaming data in HAQM DataZone with the Data Solutions Framework on AWS
In this post, we explore how AWS customers can extend HAQM DataZone to support streaming data such as HAQM Managed Streaming for Apache Kafka (HAQM MSK) topics. Developers and DevOps managers can use HAQM MSK, a popular streaming data service, to run Kafka applications and Kafka Connect connectors on AWS without becoming experts in operating it.
HAQM Web Services named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools
HAQM Web Services (AWS) has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools. We were positioned in the Challengers Quadrant in 2023. This recognition, we feel, reflects our ongoing commitment to innovation and excellence in data integration, demonstrating our continued progress in providing comprehensive data management solutions.
Streamline AWS WAF log analysis with Apache Iceberg and HAQM Data Firehose
In this post, we demonstrate how to build a scalable AWS WAF log analysis solution using Firehose and Apache Iceberg. Firehose simplifies the entire process—from log ingestion to storage—by allowing you to configure a delivery stream that delivers AWS WAF logs directly to Apache Iceberg tables in HAQM S3. The solution requires no infrastructure setup and you pay only for the data you process.
Introducing the new HAQM Kinesis source connector for Apache Flink
On November 11, 2024, the Apache Flink community released a new version of AWS services connectors, an AWS open source contribution. This new release, version 5.0.0, introduces a new source connector to read data from HAQM Kinesis Data Streams. In this post, we explain how the new features of this connector can improve performance and reliability of your Apache Flink application.
Top 6 game changers from AWS that redefine streaming data
Recently, AWS introduced over 50 new capabilities across its streaming services, significantly enhancing performance, scale, and cost-efficiency. Some of these innovations have tripled performance, provided 20 times faster scaling, and reduced failure recovery times by up to 90%. We have made it nearly effortless for customers to bring real-time context to AI applications and lakehouses. In this post, we discuss the top six game changers that will redefine AWS streaming data.