Kafka: 4 use cases and 4 real-life examples

What is Apache Kafka?

Apache Kafka is an open-source event streaming platform used for building real-time data pipelines and streaming applications. Developed by LinkedIn and later donated to the Apache Software Foundation, Kafka is designed to handle high-throughput, fault-tolerant data streaming. It’s built on a distributed architecture, allowing it to scale horizontally across multiple servers.

Kafka works on the principle of publish-subscribe, where data producers send messages to Kafka topics, and consumers retrieve them. Each topic is partitioned and replicated across multiple nodes for reliability. This design allows Kafka to process massive volumes of data with low latency, making it ideal for real-time data processing and analytics.

Kafka’s ecosystem includes Kafka Streams for stream processing, Kafka Connect for integrating with various data sources and sinks, and KSQL for running SQL-like queries on streaming data. Its wide adoption is due to its high performance, scalability, and durability, making it a popular choice for many large-scale, data-driven companies.

The need for an event streaming platform like Kafka

In today’s data-driven world, the ability to process and analyze data in real-time is crucial. Traditional data processing systems often struggle with the volume, velocity, and variety of data generated by modern applications. This is where an event streaming platform like Kafka comes into play.

Event streaming platforms enable continuous, real-time data processing. Kafka, specifically, allows organizations to ingest, store, process, and analyze data as it’s generated. This is crucial for use cases like real-time analytics, monitoring, and messaging, where immediate data processing is necessary.

Another critical aspect of Kafka is its ability to decouple data producers from consumers. This means systems can produce data at their own pace, and consumers can process it when ready. Kafka’s durability and fault tolerance ensure that data is not lost, even if a consumer is temporarily down. This reliability and flexibility make Kafka an essential tool for modern data architectures.

How Kafka supports common use cases

Let’s look at some of the main use cases for Kafka.

1. Real-Time Data Processing

Apache Kafka supports real-time data processing by providing high throughput, low-latency data handling. In this use case, Kafka functions as a central hub for data streams. Its ability to handle large volumes of data in real-time is attributed to its distributed architecture, where data is partitioned and processed in parallel across multiple nodes. Kafka ensures data integrity through replication, preventing data loss during node failures.

Kafka’s publish-subscribe model allows real-time data ingestion from various sources. Data producers publish to Kafka topics, from where data consumers can subscribe and process data as it arrives. Kafka’s performance in real-time processing is enhanced by its efficient storage mechanism and the ability to maintain high throughput, even with high data volumes. This capability is critical for applications that require immediate data analysis, such as fraud detection or live monitoring systems.

Moreover, Kafka Streams API facilitates real-time data processing by allowing developers to build applications that can process and analyze data directly within Kafka. This feature supports complex operations like windowing, aggregations, and joins on streaming data, enabling sophisticated real-time analytics directly on the stream.

2. Messaging

Kafka serves as a robust messaging system, supporting high-throughput, distributed messaging. It handles messaging use cases by allowing systems and applications to exchange data in real-time and at scale. Kafka’s durability and fault tolerance are key features here, ensuring messages are not lost in case of system failures, and can be replayed if needed.

In Kafka, messages are organized into topics, making it easier to categorize and manage different types of messages. Producers send messages to topics, and consumers subscribe to these topics to receive messages. Kafka’s ability to handle a large number of simultaneous producers and consumers makes it an ideal choice for complex messaging ecosystems.

Kafka also supports different messaging patterns, such as point-to-point, publish-subscribe, and request-reply. Its scalability allows organizations to start with a small setup and scale horizontally as their messaging needs grow. The system’s built-in partitioning, replication, and fault-tolerant design ensure messages are processed efficiently and reliably, making Kafka a popular choice for messaging in distributed systems.

3. Operational Metrics

Apache Kafka is highly effective for collecting and processing operational metrics. It captures metrics from various parts of an application or system and makes them available for monitoring, analysis, and alerting. Kafka’s distributed nature allows it to handle large volumes of metric data generated by multiple sources without performance degradation.

In this use case, Kafka acts as a central repository for operational metrics. Producers send metrics data to Kafka topics, where they are stored and made available to consumers. This setup enables real-time monitoring of operational metrics, allowing for timely insights into system performance, usage patterns, and potential issues.

Kafka’s scalability is particularly beneficial for operational metrics as it can accommodate the growing amount of data over time. The system ensures data retention for a configurable period, allowing for historical analysis of metrics. Moreover, Kafka’s compatibility with various data processing frameworks and monitoring tools enables comprehensive analysis and visualization of operational metrics.

4. Log Aggregation

Kafka is highly effective for log aggregation, a process critical for monitoring, debugging, and security analysis in distributed systems. It collects log data from various sources, such as servers, applications, and network devices, providing a centralized platform for log data management.

Kafka’s ability to handle high volumes of data makes it suitable for log aggregation, where large amounts of log data are generated continuously. Its distributed nature allows logs to be collected and processed in parallel, enhancing the efficiency of log data management. Kafka topics serve as log data repositories, where logs are categorized based on their source or type, simplifying data organization and retrieval.

The durability and fault tolerance of Kafka ensure that log data is not lost, maintaining data integrity. This aspect is crucial for log analysis, especially in scenarios involving debugging or security incident investigations. Kafka’s scalable architecture supports the increasing volume of log data as systems expand, maintaining performance without compromising data processing speed.

Tips from the expert

Andrew Mills

Senior Solution Architect

Andrew Mills is an industry leader with extensive experience in open source data solutions and a proven track record in integrating and managing Apache Kafka and other event-driven architectures

In my experience, here are tips that can help you better leverage Kafka for various real-world applications:

1. Optimize partitioning strategy: Proper partitioning of Kafka topics is crucial for maximizing performance and throughput. Ensure that partitions are aligned with your data’s access patterns, such as using customer IDs for user-based data to achieve more balanced and efficient data processing.
2. Utilize Kafka’s exactly-once semantics (EOS): For critical data pipelines, enable exactly-once processing semantics to avoid data duplication or loss. This is particularly valuable in financial applications or scenarios where data consistency is paramount, reducing the complexity of downstream deduplication.
3. Design efficient consumer groups: Avoid over-provisioning consumer groups, as this can lead to unnecessary resource usage and increased costs. Optimize the number of consumers to match the partition count for balanced load distribution, enhancing overall performance.
4. Monitor Kafka performance with JMX and observability tools: Regularly monitor Kafka brokers, producers, and consumers using JMX metrics and observability tools like Prometheus and Grafana. This helps in proactively identifying performance bottlenecks, lag issues, and system failures before they impact production.
5. Utilize Kafka Streams for microservice orchestration: Kafka Streams can be used to manage microservice interactions by acting as an intermediary layer for stateful processing. This approach simplifies the architecture by handling event-driven workflows directly within Kafka, reducing dependencies on external services.

Real world examples and uses of Kafka

Here are some real-world uses of Kafka across industries.

1. Modernized Security Information and Event Management (SIEM)

SIEM is a foundational tool in a security operations center (SOC), which collects event data from across the IT environment and generates alerts for security teams. Traditional SIEM systems often struggle with scalability and performance issues. However, Kafka’s distributed architecture allows it to handle the large-scale, high-speed data ingestion required by modern SIEM systems.

Kafka’s rapid processing capability brings a new level of responsiveness to SIEM systems. It enables organizations to detect and respond to potential security threats as they happen, rather than after the fact. This proactive approach can significantly reduce the potential damage caused by security breaches.

Real life example: Goldman Sachs, a leading global investment banking firm, leveraged Apache Kafka for its SIEM system. Kafka enabled them to efficiently process large volumes of log data, significantly enhancing their ability to detect and respond to potential security threats in real-time.

2. Website Activity Tracking

Many organizations use Kafka to gather and process user activity data on large scale websites and applications. This data can include everything from page views and clicks to searches and transactions.

Kafka enables businesses to collect data from millions of users simultaneously, process it quickly, and use it to gain insights into user behavior. These insights can help businesses optimize their websites, provide personalized user experiences, and make data-driven decisions.

Kafka’s durability is another advantage for website activity tracking. It stores data reliably for a configurable amount of time, ensuring no data is lost even if a system failure occurs. This reliability is important for businesses that need accurate data to drive their decision-making processes.

Real life example: Netflix, a major player in the streaming service industry, uses Apache Kafka for real-time monitoring and analysis of user activity on its platform. Kafka helps Netflix in handling millions of user activity events per day, allowing them to personalize recommendations and optimize user experience.

3. Stateful Stream Processing

Kafka’s stream processing capabilities make it possible to process and analyze data as it comes in, instead of batch processing it at intervals. Stateful stream processing refers to the ability to maintain state information across multiple data records. This is crucial for use cases where the value of a data record depends on previous records. Kafka’s Streams API supports this functionality.

Real life example: Pinterest utilizes Kafka for stateful stream processing, particularly in their real-time recommendation engine. Kafka’s capability to process data streams in real-time allows Pinterest to update user recommendations based on their latest interactions.

4. Video Recording

In video recording systems, Kafka serves as a buffer between the video sources and the storage or processing systems. It ingests video data in real-time, reliably stores it, and allows it to be consumed by various applications. This use case demonstrates Kafka’s ability to handle not just textual data, but also binary data like video.

Real life example: British Sky Broadcasting (Sky UK) implemented Kafka in their video recording systems, particularly for handling data streams from their set-top boxes. Kafka’s role in buffering and processing video data has been crucial for improving customer viewing experiences and content delivery.