Fully managed, cloud-based service for real-time data processing over large, distributed data streams. Amazon Kinesis can continuously capture and store terabytes of data per hour from hundreds of thousands of sources such as website clickstreams, financial transactions, social media feeds, IT logs, and location-tracking events.
With Amazon Kinesis Client Library (KCL), you can build Amazon Kinesis Applications and use streaming data to power real-time dashboards, generate alerts, implement dynamic pricing and advertising, and more.
You can also emit data from Amazon Kinesis to other AWS services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elastic Map Reduce (Amazon EMR), and AWS Lambda.
- Real-time data processing - You can continuously collect data as it is generated and promptly react to critical information about your business and operations.
- Easy to Use - You can create an Amazon Kinesis stream within seconds. With Amazon Kinesis Producer Library (KPL) and Amazon Kinesis Client Library (KCL), you can easily put data into your Amazon Kinesis stream and build Amazon Kinesis Applications for data processing.
- Parallel Processing - Option to have multiple Amazon Kinesis Applications processing the same stream concurrently. For example, you can have one application running real-time analytics and the other sending data to Amazon S3 from the same Amazon Kinesis stream.
- Elastic - he throughput of an Amazon Kinesis stream can scale from megabytes to terabytes per hour and from thousands to millions of PUT records per second. You can dynamically adjust the throughput of your stream at any time based on the volume of your input data.
- Low Cost - No upfront cost and you only pay for the resources you use. For as little as $0.015 per hour, you can have an Amazon Kinesis stream with 1MB/second ingest rate and 2MB/second egress rate.
- Reliable - Synchronously replicate your streaming data across three facilities in an AWS Region and preserves your data for 24 hours, preventing data loss in case of application failure, individual machine failure, or facility failure.
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