How to Process and Analyze Streaming Data using AWS Kinesis

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

AWS Kinesis

Why do we use AWS Kinesis?

Amazon Kinesis scales elastically, particularly for real-time data processing at a massive point. You can use these services to collect large streams of data records. Especially consumed by the application process that runs on Amazon EC2 instances. You can use it to smooth the Amazon Kinesis process and analyze data to easily get the perfect insight into information and quick answers. It also offers key capabilities at an affordable price to process a certain amount of smooth data. It takes the help of flexible tools suitable to needs and requirements through Amazon Kinesis. You can get real-time data like videos, audio, application logs, website clicks stream, machine learning, and other applications.

Amazon’s new technology will help you analyze and process data quickly rather than waiting for it to collect.

Amazon Kinesis Capabilities

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

1. Kinesis data stream

This Amazon Kinesis data stream is specifically used to create real-time custom model applications. It uses the most popular framework before the data stream process. Using the best tools, such as Apache Spark, which can be successfully run on EC2 instances. It can easily consume all stored data with data streaming prices.

2. Kinesis Data Firehouse

To capture, load, and convert data streams into the related data streams, this kinesis data firehouse is used. It stores in the AWS data stores near all the analytics with all the existing intelligence tools. These tools can continuously generate all data loads according to the destination. It 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. It has to learn all the programming languages which working with frameworks. This kinesis data analytics system is used to collect stream data. The data 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 store all the data in the stream, such as videos, photos, audio, and connected devices. Other processing can access all the video fragments and encrypt the saved data without any problems to the AWS machine learning analytics.

Advantages of AWS Kinesis

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

1. Real-time

AWS Kinesis lets you handle buffer and streaming data in real-time. In addition, it allows you to gain additional insights in seconds instead of hours or days.

2. Fully managed

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

3. Scalable

With little or no interruption, Amazon Kinesis can handle any amount of streaming content from hundreds of thousands of sources.

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 also saves all streaming video for devices equipped with Amazon Kinesis cameras. It is stored in AWS accounts in factories, public places, offices, and homes. This video streaming method can also be used to monitor security, machine learning, and face recognition, as well as other data analysis.

2. Batch to real-time analytics

Using Amazon Kinesis, you can efficiently perform real-time analytics. You can perform analytics on respective data to analyze batch processing from data warehouses through the Hadoop framework. Data leaks, data science, and machine learning are 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 easily stream all streaming data with analytics in Kinesis streams and data stored 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 service processes streaming data from IoT devices such as embedded sensors and electronic appliances. You can also use this data to send real-time alerts. It is based on programmatic behavior when the sensor surpasses the entire limit. It is best to use a sample of IoT analytics codes when creating an application.

Kinesis vs. SQS


  • Amazon Kinesis is clearly different from Amazon’s Simple Queue Service, or SQS, which makes use of Kinesis to handle big data streams in real-time.
  • Kinesis provides a routing of records using a given key ordering of records. Ability to read messages from the same stream for multiple clients. Also, simultaneously replay off messages up to as long as seven days in the past, and the ability for a client to consume records later.
  • Increased demand will not cause the Kinesis stream to scale dynamically. As a result, you’ll need to set aside enough streams in advance to meet the anticipated demand from both your data producers and data consumers.


  • This service is a messaging system that stores messages sent between distributed application components.
  • SQS provides messaging keywords so that your application can track the successful completion of tasks on the queue. You can schedule up to 15 minutes delays in messages.
  • Unlike kinesis streams, SQS will scale who meets application demand.
  • SQS has a lower number of messages that can be read or written than kinesis.
  • As a result, kinesis applications can operate with larger batches of messages than SQS applications.

Also Read: AWS Elastic Compute Cloud (EC2): Create Virtual Machine

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