How to Process and Analyze Streaming Data using AWS Kinesis

Amazon Kinesis makes it easy to process and analyze real-time streaming data so you can get timely insights and react quickly to new information.

AWS Kinesis

Why we use AWS Kinesis

Amazon Kinesis is one of the best-managed services which particularly scales elastically especially for real-time processing of the data at a massive point. These services can be used to collect large streams of data records, which are especially consumed by the application process that runs on Amazon EC2 instances. It is used to smooth the Amazon Kinesis process and analyze data so that we can easily get the perfect insight into information as well as quick answers. It is also offering key capabilities at an affordable cost to process a certain amount of seamless data with the help of flexible tools tailored to needs and requirements through Amazon Kinesis. You can also get real-time data like videos, audio, application logs, as well as website clicks stream, machine learning, and other applications too.

This new technique by Amazon will help you to analyze and process the data instantly instead of waiting for as long as after collecting the data.

Amazon Kinesis Capabilities

  1. Kinesis Data stream
  2. Kinesis Data Delivery Stream/Firehouse
  3. Kinesis Data Analytics Application
  4. Kinesis Video Stream

1. Kinesis data stream

At Amazon, this Amazon Kinesis data stream is specifically used to create real-time custom model applications, using the most popular framework before the data stream process. It can easily ingest all the stored data with all the data streaming prices by using the best tools like Apache spark, which can be successfully run on the EC2 instances.

2. Kinesis Data Delivery Stream/Firehouse

In order to capture, load and transform the data stream into the respective data streams this kinesis data firehouse is used to store in the AWS data stores near all the analytics with all the existing intelligence tools. These tools can be used to continuously generate all data loads according to the destination, which is a sustainable analytics product that provides the same analysis as streaming data.

3. Kinesis Data Analytics application

With Amazon Kinesis, Kinesis Data Analytics is the easiest way to follow all the real-time techniques with MySQL, which has to learn all the programming languages which working frameworks. This kinesis data analytics is used to capture stream data that can run with all standard queries against data streams so that analytics tools for generating alerts by answering in real time can be advanced.

4. Kinesis video stream

Amazon Kinesis video streams are used to store all the data in the stream, such as videos, photos, audio, and connected devices. To the AWS machine learning analytics, other processing can give access to all the video fragments and encrypts the saved data without any problems.

Advantages of AWS Kinesis

  1. Real-time
  2. Fully managed
  3. Scalable

1. Real-time

Amazon Kinesis enables you to process buffer and streaming data in real-time. So that you can drive insights in seconds or minutes instead of hours or days.

2. Fully managed

Amazon Kinesis fully manages and runs all your streaming applications without the need for expensive infrastructure deployment and maintenance.

3. Scalable

Amazon Kinesis can handle any amount of streaming data and process it from hundreds of thousands of sources with little or no delay.

Use Cases of Amazon Kinesis

  1. Video analytical applications
  2. Batch to real-time analytics
  3. Build real-time applications
  4. Analyzing the IOT devices

1. Video analytics applications

This application is also used to secure all streaming video for devices equipped with Amazon Kinesis cameras that are stored in AWS accounts in factories, public places, offices, and homes. This video streaming process is also used to play the video monitor the security, machine learning, and face detection along with other analytics  

2. Batch to real-time analytics

Using Amazon Kinesis, you can easily perform all real-time analytics on respective data to analyze batch processing from data warehouses through the Hadoop framework. Data leaks, data science, and machine learning are some of the most common methods used in such cases. To continuously load data, you use the Kinesis Firehouse to more frequently update all machine learning models for new and accurate data output.

3. Build real-time applications

If you want to create real-time applications, you can use Amazon Kinesis to monitor fraud detection with Live Leader results. This process can be used to easily stream all streaming data with analytics in kinesis streams and with data that is stored in the application itself with closing delays. All of these processes can help you learn more about customers, products, services, and requests so you can react quickly.

4. Analyzing the IOT devices

This Amazon Kinesis is used to process the streaming data directly from the IoT devices like the embedded sensors, TV set up boxes, and consumer appliances. You can also use this data to send real-time alerts according to actions programmatically when the sensor exceeds that entire threshold operating. It is best to use a sample of IoT analytics codes when creating an application.


Kinesis vs. SQS


  • Amazon Kinesis is separated from Amazon’s Simple Queue Service, SQS, which uses Kinesys to enable real-time processing of big data streaming.
  • Kinesis provides a routing of records using a given key ordering of records. Ability to read messages from the same stream for multiple clients, simultaneously replay off messages up to as long as seven days in the past and the ability for a client to consumed records at a later time.
  • Kinesis stream will not dynamically scale in the response to an increased demand. So you must provision enough streams ahead of time to meet the anticipated demand of both your data producers and the data consumers.

  • SQS on the other hand is used as a message queue to store the messages transmitted between distributed application components.
  • SQS provides messaging keywords so that your application can track the successful completion of tasks on the queue, and you can schedule up to 15 minutes delays in messages.
  • Unlike kinesis streams, SQS will scale automatically who meet application demand.
  • SQS has a lower number of messages that can be read or written at a time compared to kinesis.
  • So applications using kinesis can work with messages in larger batches than when using SQS.

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