Amazon Kinesis

Easily collect, process, and analyze video and data streams in real time

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 video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.

Benefits

Real-time

Amazon Kinesis enables you to ingest, buffer, and process streaming data in real-time, so you can derive insights in seconds or minutes instead of hours or days.

Fully managed

Amazon Kinesis is fully managed and runs your streaming applications without requiring you to manage any infrastructure.

Scalable

Amazon Kinesis can handle any amount of streaming data and process data from hundreds of thousands of sources with very low latencies.

Amazon Kinesis capabilities

Amazon Kinesis Data Streams

Capture, process, and store data streams


Amazon Kinesis Data Streams is a scalable and durable real-time data streaming service that can continuously capture gigabytes of data per second from hundreds of thousands of sources. 

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Amazon Kinesis Data Firehose

Load data streams into Amazon Web Services data stores


Amazon Kinesis Data Firehose is the easiest way to capture, transform, and load data streams into Amazon Web Services data stores for near real-time analytics with existing business intelligence tools.

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Amazon Kinesis Data Analytics

Analyze data streams with SQL or Apache Flink


Amazon Kinesis Data Analytics is the easiest way to transform and analyze streaming data in
real time to gain actionable insights, and respond to your business needs in real time.

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How it works

  • Amazon Kinesis Data Streams
  • Amazon Kinesis Data Firehose
  • Amazon Kinesis Data Analytics
  • Amazon Kinesis Data Streams
  • Amazon Kinesis Data Streams
  • Amazon Kinesis Data Firehose
  • Amazon Kinesis Data Firehose
  • Amazon Kinesis Data Analytics
  • Amazon Kinesis Data Analytics

Use cases


Evolve from batch to real-time analytics

With Amazon Kinesis, you can perform real-time analytics on data that has been traditionally analyzed using batch processing. Common streaming use cases include sharing data between different applications, streaming extract-transform-load, and real-time analytics. For example, you can use Kinesis Data Firehose to continuously load streaming data into your S3 data lake or analytics services. 

Example: Clickstream analytics
Amazon Kinesis allows you to evolve from batch to real-time analytics

Build real-time applications

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 Data Streams, process it using Kinesis Data Analytics, and emit the results to any data store or application using Kinesis Data 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.

Example: Analysis of streaming social media data
Amazon Kinesis analysis of streaming social media data use case

Analyze IoT device data

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 programmatically when a sensor exceeds certain operating thresholds. Use our sample IoT analytics code to build your application. No need to start from scratch.

Example: Sensors in tractor detect need for a spare part and automatically place order
Amazon Kinesis IoT device data use case