Introduction to Kafka a distributed streaming platform

You have heard the term Apache Kafka many times, today we will understand exactly what is this Apache Kafka and why the industries use Apache Kafka. Originally Kafka was developed and open sourced by LinkedIn in 2011 and later donated to the Apache Software Foundation. LinkedIn developers created Kafka to address the challenges of handling the massive amounts of data generated by their growing platform. Existing systems couldn't keep up, so Kafka was created to be fast, reliable, and able to handle massive data streams for features like live updates and recommendations.

What is Apache Kafka ?#

Apache Kafka is an open-source distributed event streaming platform designed for high-throughput, low-latency, fault-tolerant, and scalable messaging. It is widely used for building real-time data pipelines, event-driven architectures, and streaming analytics.

Apache Kafka is a publish-subscribe model in which various producers and consumers can write and read. It decouple’s source and target systems.

The Need of Apache Kafka#

From the above image you can clearly see that in distributed system your data is present at multiple locations, so you can get data, inputs, messages from different sources and you can have different targets who are consuming this data, here it is not good practise to tightly couple each target with different source because it create problem which is similar to N+1 problem, when any source is added or goes down target has to be notified about it, and that way each target and source are not independently scalable. Kafka solves this problem

In the above image you can see that the sources and targets are not tightly coupled they are independent and don’t dependent on each other, Here Kafka acts as a buffer and middleman.

Producers (sources) send their data to kafka topics, not directly to consumers (targets) and Consumers (targets) pull the data they need from kafka.

Therefore with the help of Kafka this ensures the following things :

1. Loose Coupling: Producers and consumers don’t know about each other.
2. Scalability: afka handles data distribution, so adding new sources or targets doesn’t require changes to existing systems.
3. Resilience: Kafka stores messages durably, so even if a target is temporarily unavailable, it can consume the data later.

key features of Apache kafka#

Apache Kafka stands out as a powerful distributed event streaming platform with features that make it ideal for modern data pipelines and real-time applications. Here are some of its key capabilities:

1. Scales Seamlessly to Hundreds of Nodes#

• Kafka is designed to handle massive distributed systems effortlessly.
• By adding more brokers (nodes), Kafka scales horizontally, enabling seamless growth as your data and workload requirements increase.
• Large-scale organizations rely on Kafka to handle clusters with hundreds of nodes, ensuring robust performance and fault tolerance.

2. Handles Millions of Messages Per Second#

• Kafka can process millions of messages per second while maintaining reliability and accuracy.
• Its architecture supports high concurrency, allowing multiple producers and consumers to operate simultaneously without bottlenecks.

3. Ultra-Low Latency#

• Kafka provides latency as low as 2 milliseconds, making it ideal for applications requiring near real-time processing, such as monitoring systems, fraud detection, and live data streaming.
• The low latency ensures minimal delay between producing and consuming messages.

4. Exceptional High Throughput#

• Kafka can handle throughput in the range of hundreds of MB/s while supporting 100,000s of messages per second per topic.
• Its optimized disk and network IO performance ensure consistent data flow even under heavy loads, making it a preferred choice for high-volume applications.

Use cases of Apache Kafka#

Apache Kafka’s versatility makes it a core component in various data-driven applications. Here are five key use cases where Kafka excels:

1. Real-Time Data Streaming#

• Kafka is ideal for building real-time data pipelines that deliver continuous streams of data for processing and analysis.
• Common examples include monitoring user activity, tracking IoT sensor data, and enabling live dashboards.
• With its low latency and high throughput, Kafka ensures near-instantaneous data flow between producers and consumers.

2. Log Aggregation#

• Kafka simplifies log collection by acting as a central hub where logs from distributed systems are ingested, stored, and made available for analysis.
• It is widely used to gather application logs, server metrics, and system events, enabling real-time monitoring and troubleshooting.
• Unlike traditional log aggregation tools, Kafka offers durability and scalability for handling massive volumes of logs.

3. Event Sourcing#

• Kafka’s ability to store event logs as an immutable sequence makes it an excellent choice for event sourcing.
• Events (e.g., user actions, transactions) are stored in Kafka topics and replayed later to rebuild the current state of a system or application.
• This approach is essential for implementing audit trails, ensuring data consistency, and supporting microservices communication.

4. Messaging#

• Kafka acts as a robust messaging system, enabling asynchronous communication between services in distributed architectures.
• Unlike traditional message brokers, Kafka offers higher throughput, durability, and the ability to process messages in order.
• It supports both point-to-point and publish-subscribe messaging patterns, making it versatile for various applications.

5. Batch Data Processing#

• Kafka supports batch processing by storing data for a configurable retention period, allowing downstream systems to process data in chunks at their convenience.
• Common use cases include ETL (Extract, Transform, Load) pipelines, data warehousing, and batch analytics.
• Its ability to handle both real-time and batch processing seamlessly makes Kafka a hybrid solution for diverse data workloads.

Don’t use Kafka for#

1. Simple Request-Response Communication
2. Small-scale projects
3. High Latency Tolerance
4. Monolithic Applications

Conclusion#

In this blog, we understood how Apache Kafka revolutionizes real-time data processing and distributed systems. Its ability to scale seamlessly, handle high-throughput, and ensure resilience makes it indispensable for use cases like event streaming, log aggregation, and messaging. However, it’s most effective in large-scale applications and less suited for simpler or monolithic systems.