Understanding Apache Kafka: Architecture, Components, and Real-Life Use Cases

Introduction

In today’s fast-paced digital world, data flows like water – continuously and in massive amounts. Whether it’s user interactions on a website, transactions from an e-commerce app or real-time logs from distributed systems, we need reliable ways to process, store, and manage this flow of information. This is where Apache Kafka comes in, a distributed event-streaming platform that helps organizations handle real-time data streams efficiently.

In this post, I’ll talk about Kafka’s architecture, key components and real-life use cases. We’ll also explore why, when , and how Kafka can be a game-changer for your data-driven applications.

What is Event Streaming?

Event streaming is like the nervous system of modern technology, constantly delivering information to keep everything running smoothly. It powers today’s “always-on” world, where businesses are automated and run by software, and even software interacts with other software to make decisions.

Simply put, event streaming involves capturing data in real-time from various sources—such as databases, sensors, mobile devices, cloud services, or applications as streams of events. This data is then stored securely for later use, processed in real-time or reviewed later, and sent to different systems or technologies when needed.

In short, event streaming ensures that the right data flows to the right place at the right time, allowing businesses to react quickly and make informed decisions.

Where can I use Event Streaming?

Event streaming can be use in various industries and organizations, we can include many example on that:

1. To process payment and financial transaction in real time, for example stock exchanges, bank etc.
2. To track any kind of vehicles, fleets and shipments in real-time, such as logistic and automotive industry and mobile applications.
3. To get immediate customer feedback in real time, such as e-commerce site, hotel and travel industries.
4. To serve as foundation of data platform event driven architecture and micro-services.

What is Apache Kafka?

Kafka is an open-source, distributed event-streaming platform designed to handle real-time data feeds. Originally developed by LinkedIn, it was open-sourced in 2011 and has since become one of the most widely adopted technologies for managing data streams at scale.

Think of Kafka as a centralized event log, where different systems can write and read events (or messages) in real-time.

Apache Kafka event streaming means?

Kafka provides three main features to that make it easy to handle event streaming from start to finish with one reliable solution:

1. Publish and subscribe event stream: You can write and read stream of events and move data between different system seamlessly.
2. Store event stream: Kafka can save your event data securely for as long as you need it.
3. Process event stream: You can analyze and act on events in real time as they happen or review it them later.

How works Kafka?

Already I assured you what is Kafka, but now I talk about how works it. Generally Kafka is system that lets computers talk to each other in real time with high speed connections. It can handle large number of data reliably and can run on different types of setups whether it’s physical hardware virtual machine or cloud environment. Kafka has system consist of servers and clients.

Servers:

• Kafka run as a group of servers (called cluster), which can be spread across multiple data center or cloud region.
• These server generally do two jobs: - Broker: These are servers that store and manage the data. They handle reading and writing of the event stream. - Kafka Connect: These servers help to connect Kafka with other systems like databases and allow data to flow in and out continuously.
• I any server fails, the others will automatically take over & ensuring everything keeps running without losing data.

Clients(Application and Micro-services)

• Clients are program or services that use Kafka to send or receive data, They can be spread out across many computers and can read, write, and process stream of event at the same time.
• Kafka is built to handle problem like network issue or machine failure, if one part is goes down another part will continues to work without breaking streaming.
• There are different client available for programming languages like Java, Python and Go etc.

Why Use Kafka?

Before diving into Kafka’s architecture, let’s understand why Kafka is the go-to solution for real-time data streaming:

1. Scalability: Kafka can handle millions of messages per second, making it an excellent choice for high-throughput systems.
2. Fault Tolerance: Kafka replicates data across multiple nodes, ensuring high availability and fault tolerance.
3. Low Latency: Kafka processes messages with low latency, making it ideal for real-time analytics or monitoring.
4. Decoupling Systems: Kafka provides a central platform that decouples systems, allowing them to communicate asynchronously that’s why micro-services can use it. This means one system can write data while another reads it independently.
5. Durability: Kafka can persist data on disk, so even if the system fails, your data is safe.
6. Distributed and Reliable: Kafka is distributed, which means data can be replicated across multiple servers, ensuring high availability and reliability.

Kafka’s Core Architecture

At the heart of Kafka is a publish-subscribe messaging system, where:

• Producers are client applications that publish or send messages to Kafka server.
• Consumers subscribe or read(read and process) these messages from Kafka. In Kafka Producers and Consumers are fully decoupled and agnostic to each other, this is the main reason of Kafka high scalability. For example, Producers never need to wait for consumers and other handle Consumer never care about Producers send data into Kafka or not.

Let’s break down Kafka’s architecture into its core components:

1. Topics

A Topic is essentially a named stream of messages in Kafka. Events are organized and durably store in topics. Producers write data to topics, and consumers read from topics. Topics are partitioned for scalability, allowing data to be split and processed in parallel. Very simplified at Topic is similar to Folder in file system and events are files in that folder.

Real-life Example:

In an e-commerce app, you can have topics like:

1. user-activity: capturing user clicks, logins, and navigation.
2. order-events: capturing information related to new orders, shipments, and deliveries.

2. Producers

A producer is any application that writes data (or events) to Kafka topics. Producers do not care who reads the data; they only publish to topics. Producers can send data in either synchronous or asynchronous modes.

Real-life Example:

An IoT device sending sensor data every second to a topic named device-sensors. Multiple IoT devices would act as producers.

3. Consumers

A consumer reads data from Kafka topics. Consumers can be standalone or part of a consumer group. When part of a group, consumers share the workload – each consumer in the group reads data from different partitions of a topic.

Real-life Example:

An analytics system subscribing to the user-activity topic to track and report user behaviors on your e-commerce site.

4. Brokers

A Kafka broker is a server that stores data and serves client requests (both producers and consumers). Kafka typically runs as a cluster of multiple brokers, ensuring load distribution and fault tolerance.

Real-life Example:

In a company with a global user base, you may have brokers in different regions (US, EU, Asia) to ensure high availability and distribute the load.

5. Partitions

Kafka topics are split into partitions, which allow horizontal scaling. Each partition can be placed on a different broker. A producer can choose to write to a specific partition, or Kafka can automatically assign one.

Real-life Example:

Imagine your order-events topic is divided into partitions based on the country of the user. This ensures that orders from the US are processed independently of those from Europe.

6. Zookeeper

Kafka relies on Zookeeper for managing cluster metadata and coordinating distributed systems. It keeps track of brokers, topics, and partitions, ensuring that the system remains in sync.

Real-life Example:

Zookeeper ensures that if a broker fails, the system will continue to function and Kafka will re-balance the load to other brokers.

Understanding Kafka Architecture with Analogy:

To get proper understanding on Kafka architecture now I give a analogy on that:

Imagine a Producer as a salesperson who writes and submits report files (events) to a specific folder in the file system. This folder is called a Topic, and in this case, it’s named SalesOrder On the other side, the Consumer is like an accountant who regularly visits the file cabinet (Kafka) to retrieve certain reports from the SalesOrder folder (topic) in order to process sales reports (events) and prepare financial statements.

A Broker functions like the filing cabinet itself. It stores folders (topics) and the files (events) inside them. Each filing cabinet (broker) holds a portion of the company’s folders (topics). If one cabinet (broker) breaks down, another cabinet takes over without losing any data, ensuring continuous availability.

The SalesOrder folder (topic) contains all the sales reports (events) submitted by different salespeople (producers). Each event is essentially a sales report that is placed inside the SalesOrder folder for future use.

To keep things organized, a Partition is like a sub-section within the folder. If there are too many files in the SalesOrder folder, you can split it into sections to improve organization. For example, you might divide the folder into “Partition-1” for the North Zone and “Partition-2” for the South Zone. This partitioning helps the accountant (consumer) retrieve reports more efficiently, as they can focus on a specific section of the folder rather than searching through everything.

Event Publishing in a Kafka Topic with Multiple Partitions

In the diagram above, you can visualize the Topic as a library, where P1, P2, P3, and P4 symbolize the various bookshelves within that library. Each small rectangle represents an individual book, indicating that each shelf (or partition) houses specific events (books). This structure allows for organized storage and retrieval of information. In this scenario, two producers are actively sending events (books) to designated shelves, ensuring that the library remains well-stocked with relevant and ordered data. This analogy highlights the systematic approach Kafka employs to manage and distribute events efficiently across its partitions.

how consumer working with Kafka, below I repeat this example for proper understanding on Kafka overall architecture.

How Does Kafka Work?

Kafka’s architecture revolves around the log-based approach where data is stored in append-only logs.

1. Producers send messages to a Kafka topic:

Each message is appended to the end of a partition’s log, and producers don’t worry about who’s reading the message.

2. Kafka brokers store messages in partitions:

Each partition gets a unique offset that allows consumers to track where they left off reading.

3. Consumers read messages from a Kafka topic:

Consumers can either read in real-time or replay old messages by specifying an offset. This feature makes Kafka ideal for event sourcing – where the state of a system is derived from a series of events over time.

When to Use Kafka?

• Real-time Data Streaming: If you need to process data as it arrives (for example, live transactions or user activity tracking), Kafka is a perfect fit.
• Event-Driven Architectures: In a micro-services environment, Kafka serves as a backbone to pass messages between services asynchronously, ensuring decoupling and scalability.
• Data Integration and ETL Pipelines: Kafka is often used to stream data between various systems, such as databases, Hadoop clusters, and data lakes.
• Log Aggregation and Monitoring: Companies use Kafka to collect, process, and store logs in real-time from distributed systems for monitoring and debugging.

Real-life Example:

Netflix uses Kafka for real-time monitoring and log processing. They process terabytes of logs from micro-services and make decisions on server health, scaling, and downtime, based on real-time data.

Conclusion

Kafka’s powerful architecture makes it a go-to platform for real-time data processing, integration, and event-driven systems. Whether you’re managing IoT data, building a micro-services architecture, or creating a real-time analytics pipeline, Kafka’s distributed, scalable, and fault-tolerant design will help you handle large volumes of streaming data efficiently.

When used correctly, Kafka can transform how your applications communicate and process data, ensuring that your systems remain agile, scalable, and responsive.

Why Kafka? To efficiently handle real-time data streams at scale.

When Kafka? For real-time analytics, event-driven systems, and data pipelines.

How Kafka? By leveraging Kafka’s topics, partitions, producers, and consumers in a distributed, fault-tolerant architecture.

Feel free to explore Kafka’s documentation, experiment with small use cases, and gradually scale your Kafka setup to handle more complex data pipelines. With the right use, Kafka can empower your applications to handle data streams like never before! next I will provide you Kafka basic setup with python, docker & docker-compose, so keep connected with me.