AWS Kinesis

Data is growing exponentially with time. In the world of big data, the bulk of data is generated continuously by data sources and is sent to data records simultaneously. This process is called data streaming. There are many data streaming services available, but one of the most recognized is Amazon Kinesis.

 AWS Kinesis Tutorial

AWS Kinesis Tutorial - Table of Content

What is AWS Kinesis? What is Amazon Kinesis Stream? What is AWS Kinesis Firehouse? What are the Benefits of AWS Kinesis Capabilities of Amazon Kinesis Amazon Kinesis Use Cases What is AWS kinesis Agent?

What is 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 the large streams of data records that are especially consumed by the application process that runs on Amazon EC2 instances. This Amazon Kinesis is used to collect, streamline the process and analyze the data, so easily we can get the perfect insights as well as the quick response with respect to the information.

It is also offering the key capabilities at a cost-effective price in order to process the streamlined data at a particular scale with the help of flexible tools according to the needs and requirements.

Through Amazon Kinesis, you can also get real-time data like video, audio, application logs as well as the website clickstreams, machine learning, and other applications too. This new technique by Amazon will enable you to analyze and process the data instantly instead of waiting long hours after collecting the data.

The Amazon Kinesis is also well used to solve many issues, which is officially launched in November 2013 during the reinvent conferences. The kinesis is specifically designed to collect the data from the thousands and hundreds of different resources by getting them under one roof by filtering the group, aggregating, and performing simple manipulations while transferring the data from the source location to the end location.

Amazon Kinesis is here to get enable the process and analyze data shortly after arrival itself and responds in real-time instead of having to wait until the data is collected before the process has begun only. It is highly scalable and supports the proof-of-concept or else evaluation.

1. Real-Time: Amazon Kinesis enables you to ingest, buffer and process data in real-time. One can easily derive insights in just a few seconds or else minutes.

2. Fully Managed: Amazon Kinesis can easily run the streaming applications and can be fully managed without any requirement of infrastructure management.

3. Scalable: Amazon Kinesis can easily handle any amount of streaming data and can easily process data from thousands of sources with a low level of latencies.           

What is Amazon Kinesis Stream?

This Amazon Kinesis Stream is mostly used to collect and process the massive amount of data records in real-time. One can easily create data processing applications that are called Amazon Kinesis Streams Applications. The typical kinesis stream applications read data from the Kinesis stream as the data records. The processed data records can easily be sent on the dashboards that can easily generate alerts, data can also be sent with a variety of other AWS Overview, used to generate alerts in a dynamic way.

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Here are the given typical scenarios in the usage of Amazon Kinesis Stream:

1. Accelerated Log and Data Feed Intake and Processing

The Producers can easily push the data into the stream in a direct way. One can easily push systems and application logs and can be processed with ease. It mostly prevents the log data from being lost for the front end or else the application server fails. It mostly provides the accelerated data that helps to feed intake which can easily lead to the data on the servers.

2. Real-Time Metrics and Reporting

One can easily use data collected by using Kinesis Streams with simple data analysis and reporting in real-time. One can easily process the data applications processing and can work on metrics and report for system and application logs completely streams to the data.

3. Real-Time Data Analytics

This Real-Time Data Analytics mostly combines the power of parallel processing by the usage of the value of real-time data. One can easily process website clickstreams with real-time scenarios. Analyzing site usability engagement by using multiple various kinesis streams applications to run in a parallel way.

4. Complex Stream Processing

One can easily create Directed Acyclic Graphs with Amazon Kinesis streams applications and also data streams. It mostly involves putting data from multiple Amazon Kinesis Streams applications into another stream with downstream processing with different applications of Amazon Kinesis Streams Applications.

What is AWS Kinesis Firehose?

Amazon Kinesis Firehose is a completely fully managed service to deliver real-time streaming data to destinations like Amazon S3 (Simple Storage Service), Amazon Elasticsearch Service, or else Amazon Redshift. It is entirely part of the Kinesis Streaming data platform with Amazon Kinesis Analytics and Kinesis Streams.

With the help of Kinesis Firehose, one can easily write applications or else manage resources. Data Producers can be easily configured to send data to Kinesis Firehose that can automatically deliver the data to the required destination field. You can also easily configure  Kinesis Firehose to transform the data before the data deliver itself.

The main key concepts of Kinesis Firehose are:

1. Kinesis Firehose Delivery Stream
2. Record
3. Data Producer
4. Buffer Size and Buffer Interval

Related Article: Learn AWS Interview Questions and Answers

What are the Benefits of AWS Kinesis?

The main benefits of the AWS Kinesis are here given below:

1. Real-Time: Kinesis Streams delivers real-time data processing in a reliable and flexible manner. after generating the data, one can easily collect continuously and promptly react to the complex business information and various operations in an optimized way.

2. Easy to Use: In just a few seconds, Kinesis Stream is created. The required data can be easily placed in the Kinesis stream with the help of Kinesis Producer Library and Kinesis Client Library and can build Kinesis applications for the data processing.

Elastic: The throughput of the Amazon Kinesis stream that can easily scale up from megabytes to terabytes in just a few seconds.

Parallel Processing: It mostly helps to have multiple Kinesis Applications processing with the same stream in a concurrent way. you can easily have one application that can run through real-time analytics and other sending data to Amazon s3.

Low Cost: Kinesis Streams has no upfront cost and the payment will be done only for the resources that are used.

Reliable: Kinesis Streams that replicate with multiple facilitates in the AWS Region. The data can be preserved for 24 hours and prevent data loss in case of a machine or else application failure. 

3. Fully Managed: It is fully managed and can run easily by streaming all the applications without any need for infrastructure.

4. Scalable: This is very easy to handle all the amount of streamed data with the thousands and hundreds of sources with just low latency.

• It can automatically create all the streaming data with the perfect copies that are available in all the zones along with the durability and backup options
• You can easily incorporate all the data with the respective AWS service without using the proper connectors that can reduce the data latency.
• The Amazon Kinesis will have unlimited data storage with all the capabilities by using the best leveraging services.
• All the data streamed processes can be run and set up within a couple of seconds without requiring more than that.
• In order to scale all the processing power down and up within the seconds,
• In order to maintain all the data streamed flow data easily according to it.

Capabilities of Amazon Kinesis:

                    

1. Amazon Kinesis video streams:

The Amazon Kinesis video streams are used to secure all the stream data like videos, photos, and the connected devices to the AWS for machine learning, analytics and other processing, which can give access to all the video fragments and encrypts the saved data without any problems.

2. Amazon Kinesis Data Streams:

This Amazon Kinesis data stream in Amazon is specifically used to build the real-time, custom model applications by preceding the data stream process by using the most popular frameworks.

It can easily ingest all the stored data with the data streaming prices by using the best tools like Apache Spark that can be run successfully on the EC2 instances.

3. Kinesis Data Firehouse:

In order to capture, load, and transform the data streams into the respective data streams, this Kinesis data firehouse is used to store in the AWS data Store near all the analytics with all the existing intelligence tools.

These tools can be used to prepare all the loads of the data continuously according to the destination with the durable for analytics, which gives an output like analyzing the streaming data.

5. Kinesis Data Analytics:

The Kinesis Data Analytics in the Amazon Kinesis is one of the easiest ways in order to process all the real-time techniques with SQL that has to learn all the programming languages with processing frameworks.

This kinesis data analytics is used to capture the streamed data that can run with all the standard queries against the data streams in order to precede the analytical tools for creating alerts by responding to them in real-time.

Amazon Kinesis Use Cases

1. Video analytical applications:

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

2. Batch to real-time analytics:

Using this Amazon Kinesis, you can also easily perform all the real-time analytical steps on the respective data to analyze the batch processing from the data warehouses through Hadoop frameworks. Data lakes, Data sciences, and machine learning are one of the most common methods used in these cases. In order to load the data continuously, you make use of the Kinesis Firehouse to update all the machine learning models more frequently for the new and accurate data outputs.

3. Build real-time applications:

If you want to build real-time applications, yo

u can also use this Amazon Kinesis in order to monitor fraud detection along with living leader results. This process can be used to ingest all the streaming data easily to the Kinesis streams with the analytics and the data that is stored in the application itself with the end-to-end latency. All these processes can help to learn more about the clients, products, services, and applications to react immediately.

4.  Analyzing the IoT devices:

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

Related Article: What is AWS IoT

What is AWS Kinesis Agent?

AWS Kinesis Agent is considered as the stand-alone Java software application that offers an easy way the collection and send data to Kinesis Firehose. Currently, the agent supports various processing options such as SINGLE LINE, CSVTOJSON, and LOGTOJSON.

AWS Kinesis Analytics  and AWS Kinesis Pricing

When you go for pricing, these Amazon Kinesis Streams go for the pricing. AWS Kinesis pricing is mostly based on the core dimensions Shard Hour and PUT Payload Unit and optimal dimensions extended data retention. There will also be an hourly rate based on the average number of kinesis processing units. This Amazon Kinesis Analytics helps in automatic and elastic scale with the required number of KPU's to complete the analysis models.

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