Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as application logs, website clickstreams, IoT telemetry data, and more into your databases, data lakes and data warehouses, or build your own real-time applications using this data. Amazon Kinesis enables you to process and analyze data as it arrives and respond in real-time instead of having to wait until all your data is collected before the processing can begin. Request support for your proof-of-concept or evaluation »
Amazon Kinesis enables you to ingest, buffer, and process data in real-time, so you can derive insights in seconds or minutes instead of hours or days.
Amazon Kinesis is fully managed and runs your streaming applications without requiring you to manage any infrastructure.
Amazon Kinesis can handle any amount of streaming data and process data from hundreds of thousands of sources with very low latencies.
Build custom applications that process and analyze streaming data
With Amazon Kinesis, you can perform real-time analytics on data that has been traditionally analyzed using batch processing in data warehouses or using Hadoop frameworks. The most common use cases include data lakes, data science and machine learning. You can use Kinesis Firehose to continuously load streaming data into your S3 data lakes. You can also update machine learning models more frequently as new data becomes available, ensuring accuracy and reliability of the outputs.
Zillow uses Kinesis Streams to collect public record data and MLS listings, and then update home value estimates in near real-time so home buyers and sellers can get the most up to date home value estimates. Zillow also sends the same data to its S3 data lake using Kinesis Firehose, so that all the applications can work with the most recent information. Read the case study »
Hearst built a clickstream analytics solution using Kinesis Streams, Kinesis Firehose, and Amazon Elasticsearch Service to transmit and process 30 terabytes of data a day from 300+ Hearst websites worldwide. With this platform, Hearst is able to make the entire data stream—from website clicks to aggregated data—available to editors in minutes. Read the case study »
You can use Amazon Kinesis for real-time applications such as application monitoring, fraud detection, and live leader-boards. You can ingest streaming data using Kinesis Streams, process it using Kinesis Analytics, and emit the results to any data store or application using Kinesis Streams with millisecond end-to-end latency. This can help you learn about what your customers, applications, and products are doing right now and react promptly.
MLBAM built its Statcast service using Amazon Kinesis to analyze petabytes of data each season and deliver real-time information that helps fans, broadcasters, and teams better understand the dynamics and athleticism of major league baseball games. Watch the video »
Netflix uses Amazon Kinesis to monitor the communications between all of its applications so it can detect and fix issues quickly, ensuring high service uptime and availability to its customers.
You can use Amazon Kinesis to process streaming data from IoT devices such as consumer appliances, embedded sensors, and TV set-top boxes. You can then use the data to send real-time alerts or take other actions programatically when a sensor exceeds certain operating thresholds.
Sonos uses Amazon Kinesis to monitor 1 billion events per week from wireless hi-fi audio devices, and delivers better listening experience to its customers. Watch re:Invent session »
Hello Inc. collects and processes real-time data from embedded sensors in mattresses with Kinesis Streams, and uses Kinesis Firehose to load the processed information into Amazon Redshift. Hello’s customers can monitor their sleep patterns, conditions that influence sleep such as room temperature and humidity, and ultimately get better sleep. Read the case study »
For information about all the new features in Amazon Kinesis, see the what's new page »
In 2016, AWS introduced the EKK stack (Amazon Elasticsearch Service, Amazon Kinesis, and Kibana, an open source plugin from Elastic) as an alternative to ELK (Amazon Elasticsearch Service, the open source tool Logstash, and Kibana) for ingesting and visualizing Apache logs. One of the main features of the EKK stack is that the data transformation is handled via the Amazon Kinesis Firehose agent. In this post, we describe how to optimize the EKK solution—by handling the data transformation in Amazon Kinesis Firehose through AWS Lambda.
Wouldn’t it be great if there were a user-friendly tool to generate test data and send it to Amazon Kinesis? Well, now there is—the Amazon Kinesis Data Generator (KDG).
Amazon Kinesis Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service (Amazon ES). In this post, I introduce data transformation capabilities on your delivery streams, to seamlessly transform incoming source data and deliver the transformed data to your destinations.
In this post, we show an analytics pipeline which detects anomalies in real time for a web traffic stream, using the RANDOM_CUT_FOREST function available in Amazon Kinesis Analytics.
For a full list of blog posts related to Amazon Kinesis and real-time streaming analytics, see the blog posts page »
It's easy to get started with Amazon Kinesis. Just sign up for an AWS account, or if you already have one, sign in to the AWS Management Console, and launch Amazon Kinesis.
