What is AWS Kinesis? Design Pattern, Use Cases & Comparison

We’re living in the age of cross-application integrations, instant notification, and instantaneous data updates. In such a scenario, it becomes more important to create, maintain, and modify real-time systems. 

Through the years, there have been various useful tools developed to help with building and maintaining such cross-platform systems. RabbitMQ, Kafka, and AWS Kinesis are three such tools that have helped developers and engineers seamlessly work with real-time data. These systems were all created and maintained, keeping different aims in mind. Therefore, they come with their distinct benefits and limitations based on the job at hand. 

This article will talk in detail about AWS Kinesis and how it works. 

Kinesis is a streaming service built on top of AWS. It can be used to process all kinds of data – from logs, IoT data, video data, basically any data format. This allows you to run different machine learning models and processes on the data in real-time as it flows through your system. Hence, it reduces the hassle of going through traditional databases while increasing the overall efficiency. 

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The Pub/Sub Design Pattern

Before we dive deeper into exactly how Kinesis can be used, it is essential to know a bit more about the design model it uses. In this case, we are talking about the publisher and subscriber design, which is often referred to as the pub/sub design pattern. This design pattern was developed to have the Publisher – the message’s sender, push events into Kinesis – an event bus. Then, this event bus successfully distributes the input data to all the subscribers. 

One key element to keep in mind here is that the publishers essentially have no idea that any subscribers exist. All of the messaging and transportation of messaging is managed entirely by AWS Kinesis. 

Put differently, the pub/sub design pattern is used for efficient communication of messages without creating a much-coupled design. Instead, Kinesis focuses on utilising independent components and building an overall distributed workflow out of that. 

In essence, AWS Kinesis is a powerful streaming tool that offers distinct advantages, especially compared to other real-time streaming tools. One such benefit is that it is a managed service, so developers don’t have to handle the system administration. This allows developers to focus more on their code and systems and less on administrative duties. 

Now, let’s look at some use cases of Kinesis. 

Real-Time Data Processing and Analysis

AWS Kinesis provides a complete platform for ingesting, processing, and analyzing streaming data. Real-Time Data Processing and Analysis. Businesses can process a variety of data types with their help, including logs, social media feeds, sensor data, and more. Kinesis can manage data streams of any magnitude because of its scalable infrastructure, which also provides high availability and fault tolerance.

Integration with Analytical Tools

AWS Kinesis‘ smooth interaction with various analytical tools is one of its main advantages. Businesses may execute real-time analytics, extract valuable insights, and make timely choices by using services like AWS Lambda, Amazon Kinesis Data Analytics, and Amazon Kinesis Data Firehose. The integration enables sophisticated analytics and machine learning capabilities by enabling real-time data transformations, aggregations, and enrichments.

Scalability and Flexibility

The unmatched scalability provided by AWS Kinesis enables companies to handle data streams of any magnitude without worrying about infrastructure administration. The service scales itself automatically depending on the volume of incoming data, ensuring seamless and uninterruptible data processing. In accordance with business needs and regulatory regulations, Kinesis also provides freedom in determining the proper amount of durability and retention time for the data streams.

Real-World Applications

Several industries are using AWS Kinesis to perform real-time analytics. Kinesis is used by e-commerce businesses to personalize recommendations, acquire real-time information about client behavior, and spot fraud. In order to analyze viewer preferences and deliver personalized content in real time, media and entertainment organizations use Kinesis. Kinesis is used by IoT-driven industries to instantly process and analyze sensor data, enabling proactive maintenance and real-time monitoring.

Data Partitioning

AWS Kinesis uses data partitioning to spread data among numerous shards and allow for the concurrent processing of data streams. Kinesis ensures effective consumption and scaling of data processing activities by splitting the data. Businesses can manage large amounts of data while still achieving low latency and top performance.

Record Aggregation

AWS Kinesis provides record aggregation in order to decrease processing costs and boost effectiveness. This function allows you to combine several data records, cutting down on the number of separate processing procedures. Record aggregation also improves performance and efficiency by reducing the number of interactions with downstream applications.

Data Retention

Businesses can specify the duration of data stream retention using AWS Kinesis, balancing cost reduction with data accessibility. Organizations can meet regulatory obligations and guarantee access to historical data for analysis without racking up needless storage expenses by customizing their data retention rules.

Monitoring and Alerting

Real-time data operations require strong monitoring and alerting capabilities, which AWS Kinesis offers. Businesses can use CloudWatch metrics to monitor shard-level metrics, follow stream activity, and set alarms for particular thresholds. Organizations can minimize any possible disruption by continuously monitoring Kinesis streams for faults and quickly resolving them.

Use Case of AWS Kinesis – Streaming Data

AWS Kinesis is useful for large and small companies looking to manage and integrate their data in different platforms. Kinesis is beneficial in large-scale and small scenarios for organisations looking to manage and integrate their data across platforms. 

Let’s look at two big use cases where companies used AWS Kinesis for seamlessly managing large amounts of real-time data. 

Netflix

Netflix uses AWS Kinesis to process multiple TBs of log data every day. Netflix needs a centralised application that logs data all in real-time. By using Kinesis, Netflix developed Dredge, which enriches content with metadata in real-time. That way, the data gets processed instantly as it passes through Kinesis. This eliminates one tedious step of loading data into a database for future processing.

Veritone

Veriton provides AI and machine learning services. It uses AWS Kinesis video streams for processing customer data. Veriton also applies ML models and AI to the content in real-time to improve it with metrics and metadata. Using this additional information, Veritone makes it easier to search Kinesis video streams by looking at audio, face recognition, tagged data, etc.

These are just two of the numerous examples of how companies today leverage AWS Kinesis to work with real-time streaming data more efficiently. 

Let’s move on to the technicalities and essential components of the AWS Kinesis stream. 

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Streams vs Firehose

AWS Kinesis offers developers two primary products – Kinetic Streams and Kinesis Firehose. 

To work with Kinesis Stream, you will need to use the Kinesis Producer Library. It will allow you to put all the real-time data into your stream. Further, you can connect this library to almost any application or process. However, Kinesis Streams is not a 100% managed service. So, the developer team will need to scale it manually when needed. Plus, the data fed into the stream will stay there for seven days. 

Kinesis Firehose is slightly simpler to implement. The data fed to Kinesis Firehose is sent to Amazon Redshift, Amazon S3, and even Elasticsearch – all using the AWS Kinesis engine. After this, you can process it as per your requirements. If the data is stored in Amazon S3 or any other AWS storage system, you can leave it there for as long as you like. 

Setting Up a Stream on Kinesis

Before you start accessing Kinesis, you must set up a stream by accessing the AWS CLI. In the command shell, enter the following command to create a stream called DataProcessingStream

–stream-name DataProcessingStream \

–shard-count 1 \

–region eu-west-1 

Creating a Streaming Pipeline with Python

Once you have set up a stream on Kinesis, you must start building the producer and consumer. Kinesis’s core components help you create an access layer to integrate other systems, software, and applications. 

In this tutorial, we will be working with the boto3 Python library to connect to Kinesis. 

Creating the Producer

Use the code mentioned below to create the producer using the Python programming language: 

import boto3

import json

import logging

logging.basicConfig(level = logging.INFO)

session = boto3.Session(region_name=’eu-west-1′)

client = session.client(‘kinesis’)

test_data = {‘data_tag’: ‘DataOne’, ‘score’: ’10’, ‘char’: ‘Database Warrior’}

response = client.put_record(

  StreamName=’DataProcessingStream’,

  Data=json.dumps({

    “data_tag”:  test_data[‘data_tag’],

    “score”:      test_data[‘score’],

    “char”:  test_data[‘char’]

  }),

  PartitionKey=’a01′

)

logging.info(“Input New Data Score: %s”, test_data)

To pull the data, you need another script for listening to the data being fed to the producers. For that, you can use ShardIterator to get access to all the data being fed into Kinesis. This way, you can access the real-time and future records in Kinesis. 

Creating the Consumer

Use the below-mentioned code to create a Python consumer: 

import boto3

import json

import sys

import logging

logging.basicConfig(level = logging.INFO)

session = boto3.Session(region_name='eu-west-1')

client = session.client('kinesis')

aws_kinesis_stream = client.describe_stream(StreamName='DataProcessingStream)

shard_id = aws_kinesis_stream['StreamDescription']['Shards'][0]['ShardId']

stream_response = client.get_shard_iterator(

    StreamName='DataProcessingStream',

    ShardId=shard_id,

    ShardIteratorType='TRIM_HORIZON'

)

iterator = stream_response['ShardIterator']

while True:

  try:

    aws_kinesis_response = client.get_records(ShardIterator=iterator, Limit=5)

    iterator = aws_kinesis_response['NextShardIterator']

    for record in aws_kinesis_response['Records']:

        if 'Data' in record and len(record['Data']) > 0:

          logging.info("Received New Data Score: %s", json.loads(record['Data']))

  except KeyboardInterrupt:

    sys.exit()

In the above example, we are only printing out the data. 

Problems with Kinesis Pipelines

Kinesis is genuinely beneficial, but it doesn’t come without challenges and shortcomings. One of the significant challenges you’ll face while working with Kinesis can be called ‘observability’. 

As you work with several AWS components, the system you create will become increasingly complex. For instance, if you use the Lambda functions as producer and consumer and connect it to different AWS storage systems, it will become very difficult to manage and track dependencies and errors. 

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In Conclusion 

It is no doubt that streaming data and working with real-time data is the need of the hour, and is only going to increase as our world produces more and more data. So, if you are interested in mastering the tricks of Kinesis, a professional course could help. 

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