AWS Kinesis Use Cases

Learn Aws Kinesis With Use Cases

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Do you wish to know and learn the attributes of AWS Kinesis with use cases? If you are looking for data processing at a faster pace, then you should definitely be aware of this concept. Amazon Web Services Kinesis is a cloud-based service that is ideally scalable and managed for a large amount of real-time data processing every second.

It is majestically built for real-time applications and is meant for developers to absorb and utilize numerable data from various sources without much hassle. The major purpose of AWS Kinesis is to obtain, store and process various data from different distributed streams. Some of the commonly distributed streams are social media feeds, event logs, and others.

Read more on What Is AWS Kinesis? From Basics to Advanced!

In this article, you will learn the core attributes of AWS Kinesis and its use cases for your knowledge and reference. If you are new to this ideology, then this article is about to educate you on the true perks of this cloud-based real-time data processing service.

Amazon Kinesis Data Streams

Impeccable Features of AWS Kinesis

Before you can go ahead and check on AWS Kinesis use cases to realize their true potential, it is important for you to learn the features first. The features of AWS Kinesis are as follows:

  • Easy & Convenient to Use – AWS Kinesis is integrated with the Amazon Dynamo Database, Amazon S3, and Amazon Redshift for providing scalability to the developers. It is also integrated with many other associated third-party products that ensure its potential to be fully managed. As a result, the ease & convenience of use is stated for AWS Kinesis. Moreover, creating a new stream, setting the requirements, and data streaming can be done at a faster pace with AWS Kinesis.
  • Real-time Processing- The best part about AWS Kinesis is that it holds the capacity to process big data in real-time. It is capable of processing hundreds of terabytes each hour! And it does the same in high volumes and from various sources. Some of the sources from which AWS Kinesis processes data in real-time are financial transactions, operating logs, event logs, social media feeds, and others.
  • Highly Cost-Efficient- All the clients under this AWS Kinesis do receive the ease of payment. They are billed within the AWS standard payment plan, ‘pay-as-you-go’! The billing amount is decided upon the amount of process data and the resources used. You can also prefer hourly payments for required throughput.
  • Build Proficient Applications- The developers get equipped with client libraries by AWS Kinesis. It is used for designing and operating specific data processing applications. The AWS Kinesis client library will be integrated into the Java application for notifying the availability of new data that is to be processed.

Read more on AWS Kinesis Data Streams vs AWS Kinesis Data Firehose!

These are some of the key features that explain the efficacy of AWS Kinesis. There is more to it which you will understand and idealize further in the article by studying the AWS Kinesis use cases.

AWS Kinesis with Use Cases

The major purpose of AWS Kinesis is to enable clients to develop customized apps that are meant to analyze or process streaming data. It breaks the barrier of pulling data from limited sources and allows you to infuse or add data from various sources at any time.

To help you understand it better, here are some of the AWS Kinesis use cases elaborated for your reference:

1. Used by the Financial Service Leaders

The financial leaders make use of AWS Kinesis for running a real-time audit. If you wish to maintain the same for every exchange order or single market, the AWS Kinesis stream can be the perfect bet. The standard customized solutions are often complex for management aspects and lack scalability. But, it is not the case with AWS Kinesis as it reduces the overall hassle and increases the pace of processing and managing data. With AWS Kinesis, the financial service leaders can capture, store and manage data of every market order conveniently. Along with that, they can also build auditing applications that will handle and process data in real-time.

2. Used by the Modern Digital Advertising Technology Companies

It is one of the most favorable AWS Kinesis use cases that is meant to help you keep track of the real-time metrics. Along with that, AWS Kinesis for digital advertising companies can help generate KPIs for enhancing the performance of diverse online advertisements. Earlier, these companies were implementing periodic processing of the batch. But, with the implementation of AWS Kinesis, the data processing is done in real-time for tracking down the metrics and generating reports as well. There is no location or device barrier for generating analytics anymore with AWS Kinesis. Digital advertising companies can now do it anywhere and anytime without restrictions. The modern or new companies are leveraging upon this service for optimizing the marketing investment and increasing the ad responses for clients.

3. Complex Processing of Stream

Amongst all the other AWS Kinesis use cases, this remains the most admired one. With Amazon Kinesis, you are enabled for the creation of Directed Acyclic Graphs of all data streams and dedicated Kinesis apps. Complex processing of stream is made easy with AWS Kinesis as it allows one/more dedicated Kinesis apps to add data to other AWS Kinesis streams for continuing with the stream processing aspects.

4. Real-time Metrics Extraction & Generation of Reports

The businesses that require tracking of real-time metrics do implement AWS Kinesis for the purpose. With it, the clients won’t have to wait for the data batches anymore as it would help you develop applications for measuring, tracking, and working on metrics and generating reports. When the companies prefer to wait for data batching, they usually experience some sort of data loss. But with the use of AWS Kinesis, the data producers can push the data into a dedicated stream right after they are produced. The application & system logs can be added in a continual manner to the stream and can be made available for processing at a faster pace.

Read more on AWS Kinesis vs Apache Kafka!

These are the major AWS Kinesis use cases that explain the true potential of it in the world of real-time data processing. There are many more such services that are meant to offer similar perks but are less efficient as compared to Amazon Kinesis. Therefore, AWS Kinesis stands out in the crowd of various cloud-based data processing service providers with its impeccable capabilities.

Capabilities of Amazon Kinesis

Capabilities of AWS Kinesis

It is evident that you are already aware of the true potential of AWS Kinesis. But, to give complete information about Amazon Kinesis, it is important for you to understand its capabilities. The capabilities include:

  1. It is used for secured streaming of video from several connected devices to Amazon Web Services for machine learning, data analytics, and other modes of processing.
  2. The AWS Kinesis Data Firehose is the easiest implementation measure for capturing, transforming, and loading the data streams to the data stores of AWS. After that, the real-time analytics will kick in along with the existing tools within the business operations.
  3. AWS Kinesis is embedded with Apache Flink and SQL for processing the data streams in real-time. There is no need for implementing new languages or frameworks for this purpose.

Conclusion

There are fewer or no limitations within the AWS Kinesis stream as it supports the processing of 1000 PUT records every second. All the companies who are into Big Data can find it as a perfect inclusion. There are more usability aspects than the mentioned use cases of AWS Kinesis above, such as the ordering of several records, reading & replaying records, and others for multiple AWS Kinesis apps.

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You also get a flexible approach towards choosing the throughput. The minimum throughput within Kinesis is 1MB/sec as input and 2MB/sec as output. The maximum throughput has no limit and can be anything that you prefer. Hence, this flexible approach is what makes AWS Kinesis the best pick for analyzing Big Data.

About Pavan Gumaste

Pavan Rao is a programmer / Developer by Profession and Cloud Computing Professional by choice with in-depth knowledge in AWS, Azure, Google Cloud Platform. He helps the organisation figure out what to build, ensure successful delivery, and incorporate user learning to improve the strategy and product further.

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