Introduction

In today's world, many applications in banking, insurance, telecom, manufacturing, and other industries have large numbers of users. These users generate huge amounts of data in real-time. Storing and processing this data for real-time analytics can be difficult and challenging. Traditional databases struggle to handle this because they can’t process and store data quickly enough. To address these issues, Apache Kafka plays an important role.

According to the official Kafka website, "more than 80% of all Fortune 100 companies trust and use Kafka" to manage their data streams.

Apache Kafka Official Website

This highlights how important Kafka is for organizations. Let’s explore some scenarios to understand how Kafka helps organizations and how it works through practical examples.

Real-Time Messaging: How Kafka Powers Instant Communication

Imagine the pressure of delivering messages instantly while ensuring they’re securely stored for future reference. In a real-time chat system like WhatsApp or Slack, users constantly send and receive messages in various groups. These messages must be delivered instantly and also stored in a database for future reference. The massive amount of data generated by users in real time can create significant traffic and load on the database. Traditional databases(SQL, NoSQL) often have low throughput, which means they struggle to handle a large number of transactions simultaneously. This is where Kafka is beneficial.

Throughput: The amount of data processed by a system in a specific time, 
usually measured in transactions or messages per second(TPS).
 High throughput means the system can handle many operations efficiently.

Real-Time Analytics in Ride-Sharing

Let’s consider another example involving Pathao. Pathao has numerous drivers providing services by picking up passengers from one location and dropping them off at another. To perform effective analytics, the system must capture detailed information about every trip, including location data (potentially updated every second), the speed of the vehicle, and the time taken for each journey. However, traditional databases often struggle with the massive amounts of data generated in real-time. This is where Kafka efficiently manages the data flow.

Disclaimer: I do not have detailed knowledge of the internal architecture of Pathao. This example is intended to illustrate a use case for Kafka.

Enhancing Food Delivery Services

As a food delivery app like Foodmandu, customers place orders and get real-time updates on the delivery person's location. The location data needs to be updated every second, along with other details like the time taken and the generated location. This information is important for storing in the database for future analysis and improvements. However, traditional databases often can't handle the large amounts of data generated at the same time by many users. This is where Kafka is essential, efficiently managing and processing this data to ensure a smooth and responsive experience for both customers and delivery personnel.

Disclaimer: I do not have detailed knowledge of the internal architecture of Foodmandu. This example is intended to illustrate a use case for Kafka.

The Dual Role of Kafka in Real-Time Data Management

From these examples, we can conclude that Kafka not only serves as a messaging system but also functions as a data store. It offers very high throughput, meaning it can handle a significantly larger volume of transactions simultaneously compared to traditional databases. This capability makes Kafka an ideal solution for applications that require real-time data processing and analytics.

Why Use Kafka Alongside Traditional Databases?

However, a question may arise: why not use Kafka instead of a traditional database?

While Kafka provides high throughput, meaning it can handle a large number of operations for storing and retrieving data, but it is not designed for long-term data storage. Kafka retains data for a limited time, which makes it less suitable for applications that require persistent historical data storage. Therefore, it is often used alongside traditional databases, where Kafka manages real-time data processing, stores data temporarily, and sends the processed outcomes to the database. The traditional database then stores this data for long-term access and analysis.

Conclusion and Next Steps

Thank you for joining me on this exploration of Apache Kafka! I hope you found the information on Apache Kafka and its applications valuable. In my next piece, I will take a closer look at the key terms used in Kafka and provide practical implementation examples through code. If you have experiences with Kafka or questions about its applications, I’d love to hear your thoughts in the comments below. Your feedback is invaluable as I continue to learn and share.

Stay tuned for my upcoming article, where we’ll explore essential Kafka terminologies that can enhance your understanding and usage of this powerful tool. Don’t forget to follow me for updates!