# Amazon Kinesis Published articles for Amazon Kinesis. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Scale down Kinesis Data Streams on-demand capacity with ODA warm throughput DevFeed: [Scale down Kinesis Data Streams on-demand capacity with ODA warm throughput]() Original publisher: [Read original article]() Author: Pratik Patel Published: 2026-09-14T15:36:36Z Content type: tutorial Language: en Sources: [AWS Big Data Blog]() Topics: [Streaming](), [Scalability](), [data](), [Amazon CloudWatch](), [monitor]() Tags: [advanced-300](), [amazon-cloudwatch](), [amazon-kinesis](), [announcements](), [cost](), [kinesis-data-streams](), [metrics](), [performance](), [scaling]() ### AI overview This AWS post explains how Amazon Kinesis Data Streams can scale down ingest capacity for On-demand Advantage streams using warm throughput. It covers the capability, monitoring with Amazon CloudWatch, and practices for reducing excess capacity after transient traffic spikes while maintaining performance. ### Source excerpt Amazon Kinesis Data Streams now supports scaling down ingest capacity for on-demand Advantage streams with warm throughput. Learn how the scale-down works, how to monitor stream behavior with Amazon CloudWatch, and best practices for releasing excess capacity after transient traffic bursts. ## How Sony LIV built real-time video streaming analytics with AWS DevFeed: [How Sony LIV built real-time video streaming analytics with AWS]() Original publisher: [Read original article]() Author: Rahul Sureka Published: 2026-09-08T16:43:05Z Content type: article Language: en Sources: [AWS Big Data Blog]() Topics: [real-time](), [Streaming](), [Data analysis](), [Amazon Web Services](), [Apache Iceberg](), [Apache Spark](), [data lake](), [data-architecture]() Tags: [acid](), [amazon-data-firehose](), [amazon-emr](), [amazon-kinesis](), [amazon-s3](), [apache-iceberg](), [apache-spark](), [aws](), [batch-processing](), [customer-solutions](), [data-architecture](), [data-streaming-analytics-on-aws-aws-blog](), [media-entertainment](), [metrics](), [playback](), [real-time](), [streaming]() ### AI overview Sony LIV built a real-time streaming analytics architecture on AWS using Amazon Kinesis Data Streams for event ingestion, Amazon Data Firehose for delivery, Amazon EMR with Apache Spark for processing, and Apache Iceberg on Amazon S3 for queryable ACID-compliant data lake tables. The system supports large-scale viewer-event processing and real-time engagement and video-quality monitoring. ### Source excerpt Real-time data analytics is transforming how streaming platforms serve their audiences. Learn how Sony LIV built a comprehensive, real-time streaming analytics solution on AWS using Amazon Kinesis Data Streams, Amazon EMR, and Apache Iceberg. ## When and how to centralize AWS PrivateLink Interface endpoints with Amazon VPC Lattice DevFeed: [When and how to centralize AWS PrivateLink Interface endpoints with Amazon VPC Lattice]() Original publisher: [Read original article]() Author: Rohit Aswani Published: 2026-09-04T21:09:12Z Content type: tutorial Language: en Sources: [Networking & Content Delivery]() Topics: [Amazon VPC](), [Amazon Web Services](), [Cloud](), [Network](), [Provisioning](), [software-architecture](), [Security]() Tags: [amazon-cloudwatch-logs](), [amazon-elastic-container-registry](), [amazon-kinesis](), [amazon-route-53](), [amazon-vpc](), [amazon-vpc-lattice](), [aws](), [aws-privatelink](), [aws-secrets-manager](), [aws-transit-gateway](), [customer-solutions](), [how-to](), [multi-account](), [networking-content-delivery](), [private-access](), [provisioning](), [technical-how-to](), [vpc]() ### AI overview This tutorial explains how to centralize AWS PrivateLink interface endpoints in large, multi-account AWS environments. It compares a routing-hub approach using Transit Gateway or Cloud WAN with a resource-access approach using Amazon VPC Lattice, then focuses on deployment in a single Region with custom domain names. ### Source excerpt In large multi-account AWS environments, teams need to centralize AWS PrivateLink interface endpoints for services by often provisioning the same endpoints separately in each Amazon Virtual Private Cloud (Amazon VPC). As the number of VPC and accounts grow, this leads to added endpoint costs, inconsistent endpoint policies and operational overhead. With centralized endpoints, you provision [...] ## Deliver real-time data to streaming tables for Apache Iceberg with Amazon Kinesis Data Streams DevFeed: [Deliver real-time data to streaming tables for Apache Iceberg with Amazon Kinesis Data Streams]() Original publisher: [Read original article]() Author: Nikit Pednekar Published: 2026-08-31T23:03:25Z Content type: release Language: en Sources: [AWS Big Data Blog]() Topics: [Streaming](), [Apache Iceberg](), [Amazon S3](), [Data pipelines](), [Apache Flink](), [AWS Glue](), [Apache Spark](), [Amazon Redshift]() Tags: [amazon-kinesis](), [amazon-s3](), [amazon-s3-tables](), [announcements](), [apache-iceberg](), [apache-iceberg-tables](), [data-pipelines](), [infrastructure](), [intermediate-200](), [kinesis](), [kinesis-data-streams](), [streaming]() ### AI overview Amazon Kinesis Data Streams introduces streaming tables, a fully managed capability that continuously delivers streaming data as queryable Apache Iceberg tables in Amazon S3 Tables. The release highlights lower delivery and query costs through inline compaction, with access from Athena, Redshift, and Spark. ### Source excerpt Amazon Kinesis Data Streams now supports streaming tables, a fully managed capability that continuously delivers your streaming data as queryable Apache Iceberg tables on Amazon S3 Tables. Streaming tables reduce data delivery costs to S3 Tables by up to 50% compared to self-managed alternatives and reduce downstream query costs by up to 30% through intelligent inline compaction that eliminates the small file problem. You need no custom applications, no self-managed compute, and no operational overhead. ## Game integrity and cheat detection using AWS Game Analytics Pipeline, Amazon Quick, and Kiro agentic IDE DevFeed: [Game integrity and cheat detection using AWS Game Analytics Pipeline, Amazon Quick, and Kiro agentic IDE]() Original publisher: [Read original article]() Author: Gage McGilvra Published: 2026-07-23T14:17:50Z Content type: tutorial Language: en Sources: [AWS for Games Blog]() Topics: [Analytics Pipeline](), [Kiro](), [dashboards](), [data-processing](), [SQL](), [Python](), [Amazon S3]() Tags: [amazon-athena](), [amazon-kinesis](), [amazon-quick-sight](), [amazon-s3](), [amazon-simple-storage-service-s3](), [analysis](), [analytics-pipeline](), [aws-management-console](), [data-analysis](), [games](), [github](), [how-to](), [industries](), [kiro]() ### AI overview This tutorial shows how to extend AWS Game Analytics Pipeline with cheat-detection capabilities. It covers collecting integrity metrics, transforming game-event data with Athena SQL views, importing datasets into Amazon Quick Sight, and building ML-powered dashboards to identify anomalous player behavior. ### Source excerpt Cheating in online games erodes player trust, damages competitive integrity, and can drive players away. Game studios need a scalable analytics pipeline to detect anomalous player behavior. In this post, we show how to build a game integrity proof of concept (POC) using the Guidance for Game Analytics Pipeline on AWS, Amazon Athena, and Amazon [...]