Blog Amazon Web Services What Is AWS Kinesis? From Basics to Advanced
What is AWS Kinesis - From Basics to Advanced

What Is AWS Kinesis? From Basics to Advanced

AWS Kinesis is one of the most popular data streaming and analytics solutions in the present. Learn more about the components and uses of Amazon Kinesis now!

Data streaming is one of the most important requirements for businesses in the present times for various reasons. First of all, data is driving the efficiency of businesses in arriving at favorable decisions regarding operations, sales, and marketing. Secondly, data management infrastructures all over the world are slowly growing in size and complexity. Therefore, the application of tools such as AWS Kinesis has been gaining momentum in recent times. 

Amazon Kinesis is a real-time, fully managed, and highly scalable cloud service for streaming large volumes of data on AWS. It has been tailored for various real-time applications with support for multiple functionalities, thereby gaining popularity among enterprises and individuals. So, let us dive deeper into the components of AWS Kinesis to understand its working better. 

What is Amazon Kinesis?

The first thing that comes to mind when speaking about AWS Kinesis basics refers to its definition. It is the data analytics service offered by Amazon Web Services and is available as a fully managed and highly scalable platform for data streaming applications on AWS. AWS Kinesis helps in easier collection, processing, and analysis of real-time, streaming data for obtaining timely insights and responding faster to new information. 

Amazon Kinesis delivers significant features for the cost-effective processing of streaming data according to the desired scale. In addition, it also enables the flexibility for selecting tools that fit the requirements of a concerned application perfectly. Amazon Kinesis helps in ingesting real-time data, including video, application logs, IoT telemetry data, audio, website clickstreams, and audio for analytics, machine learning, and a series of other applications. 

The most interesting facet about Amazon Kinesis refers to the fact that it enables the processing and analysis of data upon arrival. In addition, it also ensures that users can respond to new information quickly without having to wait for the collection of all data before starting the processing tasks. 

Components of AWS Kinesis

The next important thing that comes forward in AWS Kinesis advanced concepts refers to its components. Amazon Kinesis has found applications in notable corporations such as Netflix, especially for monitoring all communications between applications by leveraging Kinesis. As a result, it can help Netflix with faster detection and resolution of technical issues.

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In addition, AWS Kinesis also supports users for performing different tasks like ingesting and processing real-time data or developing custom streaming applications for particular requirements. So, how is Amazon Kinesis able to provide these functionalities? Let us dive into the individual components of Amazon Kinesis to understand its working effectively.

The important components that dictate AWS Kinesis advanced functionalities are as follows,

1. Kinesis Data Firehose

Amazon Kinesis Data Firehose is an important component in Amazon Kinesis for loading data streams into AWS data stores. It provides the simplest avenue for capturing, transforming, and loading data streams into AWS data stores to facilitate near real-time analytics by leveraging existing business intelligence tools.

2. Kinesis Data Streams

Amazon Kinesis Data Streams offer a proven solution for highly scalable and durable real-time data streaming for continuously capturing, processing, and storing data streams. The interesting aspect about Kinesis Data Streams refers to their ability to capture gigabytes of data every second from multiple sources.

3. Kinesis Data Analytics

Amazon Kinesis Data Analytics is also an important aspect in AWS Kinesis, especially for analyzing data streams with Apache Flink or SQL. If you don’t want to learn about a new programming language or a processing framework to start processing data streams, then Kinesis Data Analytics can serve as the ideal solution for you.

4. Kinesis Video Streams

Amazon Kinesis Video Streams are also a crucial component in Amazon Kinesis. They help in easier and secure streaming of video to AWS from connected devices for the purposes of machine learning (ML), analytics, and other processing tasks. It is basically a favorable solution for easily capturing, processing, and storing video streams. 

Evaluation of Important Services in Amazon Kinesis

With a fundamental idea of the functions of individual components in AWS Kinesis, it is easier to identify its significance. However, it is also important to take an in-depth look at the use cases of each component. So, let us go through a comprehensive evaluation of important services in Amazon Kinesis.

1. Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose provides the simplest approach for safely loading streaming data into data stores, data analytics services, and data lakes. It can help in capturing, transforming, and delivering streaming data to Amazon Redshift, generic HTTP endpoints, Amazon S3, service providers such as Splunk, Datadog, MongoDB and New Relic, and Amazon ElasticSearch. Kinesis Data Firehose also provides functionalities for batching, compression, transformation, and encryption of data streams prior to loading. Therefore, it can help in reducing the required storage capacity alongside improving the assurance of security.

Amazon Kinesis Data Firehose
Amazon Kinesis Data Firehose

Interestingly, users can create a Firehose delivery stream from the AWS Management Console, thereby implying the flexibility of using AWS Kinesis. Interestingly, users can also configure the Firehose delivery stream with few clicks and ingest streaming data from multiple data sources to desired destinations. Kinesis Data Firehose also enables the configuration of data streams for automatic conversion of incoming data to open or standard-based formats. Kinesis Data Firehose supports standard-based formats such as Apache ORC and Apache Parquet. 

The notable use cases of Amazon Kinesis Data Firehose include,

  • Kinesis Data Firehose can help in continuously capturing data from connected devices and loading them into specified destinations.
  • Kinesis Data Firehose can help in ingesting real-time clickstream data for ensuring that marketers can connect with their customers effectively. 
  • Users can also collect, monitor, and analyze log data in real-time for faster detection of application errors. 

Kinesis Data Firehose offers support for Splunk as one of its destinations. So, users could capture and transfer network traffic flow logs to Kinesis Data Firehose for transforming, enriching, and loading data to Splunk. 

2. Amazon Kinesis Data Streams

Kinesis Data Streams or KDS is also one of the notable entries in discussions on AWS Kinesis basics. Amazon KDS basically offers a highly scalable and massively durable real-time data streaming service. It can support capabilities for continuously capturing multiple gigabytes of data every second from multiple sources.

Amazon Kinesis Data Streams
Amazon Kinesis Data Streams

Amazon Kinesis Data Streams supports various sources such as social media feeds, website clickstreams, IT logs, database event streams, location-tracking events, and financial transactions. Interestingly, Amazon Kinesis Data Streams ensure that collected data is available within milliseconds for real-time analytics use cases. 

AWS Kinesis Data Streams is suitable for the following use cases,

  • Amazon KDS can help in collecting log and event data from various sources such as mobile devices, desktops, and servers. Subsequently, users could develop Kinesis applications for continuous data processing, generating metrics, delivering aggregated data in stores such as Amazon S3, and empowering live dashboards.
  • Users can run real-time analytics on high-frequency event data collected by Kinesis Data Streams by leveraging Kinesis applications. 
  • It is also possible to push mobile application data to Kinesis Data Streams from multiple devices, thereby ensuring the availability of data upon its production on mobile devices. 
  • Kinesis Data Streams could help in collecting data related to player-game interactions continuously, followed by feeding data to gaming platforms. As a result, it can help in designing games with dynamically engaging experiences. Developers could tailor gaming experiences on the basis of player behaviors and actions.

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3. Amazon Kinesis Data Analytics

Amazon Kinesis Data Analytics is one of the notable AWS Kinesis advanced concepts that are important for all users. It is fundamentally the simplest tool for real-time transformation and analysis of streaming data by leveraging Apache Flink. Amazon Kinesis Data Analytics helps in lowering the complexity in the development, management, and integration of Apache Flink applications with different AWS services.

Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics

It provides the flexibility for continuously running streaming applications alongside scaling automatically for matching throughput and volume of incoming data. Interestingly, Amazon Kinesis Data Analytics does not impose the need for managing servers or the burden of minimum costs. 

AWS Kinesis Data Analytics is suitable for the following use cases,

  • Amazon Kinesis Data Analytics helps in developing streaming ETL or Extract-Transform-Load applications with in-built operators. Users can also deliver data quickly to Amazon Managed Streaming for Apache Kafka, Amazon S3, Amazon Kinesis Data Streams, custom integrations, and Amazon ElasticSearch Service by using the in-built connectors.
  • Amazon Kinesis Data Analytics can also help in developing applications for processing events from one or multiple data streams. It can also help in building applications with triggers for conditional processing alongside external actions. 
  • Amazon Kinesis Data Analytics also helps in interactive querying of streaming data by using standard SQL. It also supports analytics with a facility for building Apache Flink applications and Apache Beam applications for analyzing data streams.

4. Amazon Kinesis Video Streams

AWS Kinesis Video Streams is one of the latest additions in the components of Amazon Kinesis. It helps in secure video streaming from connected devices to AWS for the objectives of playback, analytics, other processing tasks, and machine learning. Kinesis Video Streams help in automatically provisioning and elastic scaling of infrastructure required for ingesting streaming video data from multiple devices. It supports the durable storage, encryption, and indexing of video data in the users’ streams.

Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics

In addition, it also supports flexible access to data by leveraging easy-to-use APIs. Kinesis Video Streams can help in video playback for on-demand and live viewing. They can also help in building applications with the power of video analytics and computer vision by integrating Amazon Rekognition Video. Kinesis Video Streams also support the integration with libraries for ML frameworks such as TensorFlow, OpenCV, and Apache MxNet. 

The use cases of Amazon Kinesis Video Streams are primarily evident in the following applications.

  • Kinesis Video Streams are ideal choices for easily streaming live audio and video from home devices equipped with cameras to AWS. Subsequently, the streams can help in developing a wide range of smart home applications 
  • Amazon Kinesis Video Streams can help in ingesting, storing, and analyzing the massive volume of video data generated from the multiple cameras installed at traffic lights, shopping malls, parking lots, and other public venues. 
  • AWS Kinesis Video Streams can help in collecting a wide range of time-encoded data from industrial equipment. The data can include RADAR and LIDAR signals, depth data from industrial equipment, and temperature profiles. Enterprises could then analyze the data with desired machine learning framework for the concerned industrial automation use cases.     

Conclusion

So, you can clearly notice the different components of AWS Kinesis and how it works for different applications. The outline of functions and use cases of each component in Amazon Kinesis delivers a profound idea of its significance. Furthermore, the evolution of Amazon Kinesis Video Streams as a new component in Amazon Kinesis widens its functionality.

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With features such as ease of use and cost-efficiency alongside higher scalability, Amazon Kinesis can bring considerable value. Therefore, professionals with expertise in using Amazon Kinesis can help in developing highly functional data analytics solutions. As the demand for efficient data processing, analysis, and storage mechanisms continues to increase, it is important to start learning about Amazon Kinesis right now!

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