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Receive twelve months of access to the AWS Free Usage Tier and enjoy AWS Basic Support features including, 24x7x365 customer service, support forums, and more.

Please note that Amazon Kinesis is not currently available on the AWS Free Usage Tier.

Amazon Kinesis is a fully managed service for real-time processing of streaming data at massive scale. Amazon Kinesis can collect and process hundreds of terabytes of data per hour from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

With Amazon Kinesis applications, you can build real-time dashboards, capture exceptions and generate alerts, drive recommendations, and make other real-time business or operational decisions. You can also easily send data to a variety of other services such as Amazon Simple Storage Service (Amazon S3), Amazon DynamoDB, or Amazon Redshift. In a few clicks and a couple of lines of code, you can start building applications which respond to changes in your data stream in seconds, at any scale, while only paying for the resources you use.

Introduction to Amazon Kinesis (2:08)


Amazon Kinesis enables you to collect and analyze information in real-time, allowing you to answer questions about the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report.

You can create a new stream, set the throughput requirements, and start streaming data quickly and easily. Amazon Kinesis automatically provisions and manages the storage required to reliably and durably collect your data stream.

Amazon Kinesis seamlessly scales to match the data throughput rate and volume of your data, from megabytes to terabytes per hour. Amazon Kinesis will scale up or down based on your needs.

With Amazon Kinesis, you can reliably collect, process, and transform all of your data in real-time before delivering it to data stores of your choice, where it can be used by existing or new applications. Connectors enable integration with Amazon S3, Amazon Redshift, and Amazon DynamoDB.

Amazon Kinesis provides developers with client libraries that enable the design and operation of real-time data processing applications. Just add the Amazon Kinesis Client Library to your Java application and it will be notified when new data is available for processing.

Amazon Kinesis is cost-efficient for workloads of any scale. You can pay as you go, and you’ll only pay for the resources you use. You can get started by provisioning low throughput streams, and only pay a low hourly rate for the throughput you need.

Amazon Kinesis can collect the high throughput volumes of data generated by your applications, infrastructure operations, and mobile devices -- and make it available for fast identification of exceptions, slow queries, page views, click-through paths, or resource utilization.

Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. You can also create applications that act on windows of time, such as customer behavior over the last 5 minutes.

With Amazon Kinesis, you can easily capture and process the wealth of information flowing through social media to identify changing trends, evaluate social graph dynamics, and provide analytics on sentiment and sharing behavior.

Collect and analyze your financial information minute by minute, at any scale, instead of having to wait until the end of the business day. You can immediately respond to anything from a new trade to changes in value at risk.

Keep up to speed with how the players are interacting with your game and each other, and dynamically deliver a more engaging experience.

By capturing data such as purchase orders, click-streams, and social media trends, your applications can dynamically adjust their machine learning-based recommendation and ranking algorithms.