Amazon Kinesis Data Streams - Data Streaming Service

Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service. KDS can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. The data collected is available in milliseconds to enable real-time analytics use cases such as real-time dashboards, real-time anomaly detection, dynamic pricing, and more.

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What is Amazon Kinesis Data Streams? (2:08)

Benefits

Real-time performance

Make your streaming data available to multiple real-time analytics applications, to Amazon S3, or to AWS Lambda within 70 milliseconds of the data being collected.

Durable

Reduce the probability of data loss. Synchronous replication of your streaming data across three Availability Zones in an AWS Region and store that data for up to 365 days to provide multiple layers of protection from data loss.

Secure

Meet your regulatory and compliance needs by encrypting sensitive data within KDS, and privately accessing your data via your Amazon Virtual Private Cloud (VPC). Data can be secured at-rest by using server-side encryption and AWS KMS master keys.

Easy to use

Build your streaming applications quickly using the AWS SDK, the Kinesis Client Library (KCL), connectors, and agents. Easily process data with built-in integrations to AWS Lambda, Amazon Kinesis Data Analytics, Amazon Kinesis Data Firehose, and AWS Glue Schema Registry.

Elastic

Dynamically scale your applications. Kinesis data streams scale from megabytes to terabytes per hour, and scale 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

Kinesis Data Streams has no upfront cost, and you only pay for the resources you use. For as little as $0.015 per hour, you can have a Kinesis data stream with 1MB/second ingest and 2MB/second egress capacity.

How it works

Use cases

Log and event data collection

Kinesis Data Streams can be used to collect log and event data from sources such as servers, desktops, and mobile devices. You can then build Kinesis Applications to continuously process the data, generate metrics, power live dashboards, and emit aggregated data into stores such as Amazon S3.

Real-time analytics

You can have your Kinesis Applications run real-time analytics on high frequency event data such as sensor data collected by Kinesis Data Streams, which enables you to gain insights from your data at a frequency of minutes instead of hours or days.

Mobile data capture

You can have your mobile applications push data to Kinesis Data Streams from hundreds of thousands of devices, making the data available to you as soon as it is produced on the mobile devices.

Learn about Kinesis Data Streams and the AWS Mobile SDK Integration (iOS | Android)

Gaming data feed

Kinesis Data Streams can be used to continuously collect data about player-game interactions and feed the data into your gaming platform. With Kinesis Data Streams, you can design a game that provides engaging and dynamic experiences based on players’ actions and behaviors.

Case studies

Netflix uses 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. Read the case study.

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Zillow uses Kinesis Data 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. Read the case study.

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Thomson Reuters built a solution using Amazon Kinesis to gain insights from data generated by its offerings and continuously improve the user experience. Read the case study.

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Hearst built a clickstream analytics solution using Amazon Kinesis to transmit and process 30 terabytes of data a day from 300+ Hearst websites worldwide. Read the case study.

Read the case study