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. 

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. 

upGrad’s Master of Science in Machine Learning and AI, offered with the collaboration of IIIT-B and LJMU, is an 18-month comprehensive course designed to help you start with the very basics of data exploration and reach all the critical concepts of NLP, Deep Learning, Reinforcement Learning, and more. What’s more – you get to work on industry projects, 360-degree career support, personalised mentorship, peer networking opportunities, and a whole lot more to help you master Machine Learning & AI. 

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