Apache Kafka Use Cases: When to Choose It and When to Look Elsewhere

What Is Apache Kafka?

Apache Kafka is an open-source distributed event streaming platform designed to handle real-time data feeds. It is widely used by organizations that require scalable, fault-tolerant messaging systems for processing, storing, and analyzing large volumes of streaming data efficiently. Whether tracking stock prices, user behavior, or system logs, Kafka is built to handle high-throughput event processing with low latency.

However, Kafka is not a one-size-fits-all solution. While it offers powerful capabilities, it has a learning curve and operational overhead that may not be suitable for every use case. Understanding where Kafka excels and where it may not be ideal is essential before adopting it.

Key Features of Apache Kafka

Kafka differentiates itself from traditional messaging systems through its unique features:

These capabilities make Kafka a backbone technology for event-driven architectures across various industries.

Why Does Kafka Matter?

Traditional batch processing systems collect data over time and process it in bulk. While effective for some applications, batch processing cannot meet the real-time demands of industries that require immediate insights. Kafka enables continuous event streaming, providing real-time data ingestion and processing at scale.

Industries that heavily rely on Kafka include:

• Financial Services: Banks and trading firms use Kafka for real-time risk management, fraud detection, and trade monitoring.

• Retail and E-commerce: Online marketplaces track customer behavior in real-time to optimize recommendations, promotions, and inventory.

• Healthcare: Kafka is used to stream patient data from medical devices to monitoring systems for immediate response to critical conditions.

• Telecommunications: Network providers use Kafka for call data record analysis, monitoring service quality, and predicting outages before they impact customers.

• Media and Gaming: Streaming platforms and online games process massive volumes of user interactions, leaderboards, and personalized content recommendations.

That said, Kafka is not ideal for every scenario. For low-volume messaging or strict ordering with message acknowledgment guarantees, alternative messaging systems may be more appropriate.

How Apache Kafka Works

Apache Kafka is a distributed event streaming platform that follows a publish-subscribe architecture. In this model, producers generate messages that are stored in topics, and consumers subscribe to those topics to receive data in real-time. This decoupling of producers and consumers ensures that multiple consumers can process data independently without impacting other components.

Key Components of Kafka

1. Producers – Applications that publish messages to Kafka topics.

2. Brokers – Servers that store and manage messages, distributing them across partitions.

3. Topics – Logical channels where messages are categorized and stored.

4. Partitions – Each topic is split into multiple partitions to enable parallelism and improve scalability.

5. Consumers – Applications that subscribe to topics and process messages in real-time.

6. Consumer Groups – A collection of consumers that work together to distribute and process data efficiently.

7. ZooKeeper (or KRaft) – Kafka 3.3+ introduces KRaft, eliminating the need for ZooKeeper. However, KRaft is still stabilizing, and some enterprises continue using ZooKeeper for mature deployments. Full adoption of KRaft is still evolving.

By partitioning data across multiple brokers and balancing consumers within groups, Kafka achieves high throughput and fault tolerance, making it an ideal choice for real-time data streaming applications.

Kafka in the Ecosystem

Apache Kafka integrates seamlessly with a variety of big data and analytics tools, acting as the central nervous system for data movement in large-scale environments. Some key integrations include:

• Hadoop – Streams data into HDFS for long-term storage and batch analytics.

• Apache Spark – Enables real-time data processing for use cases like fraud detection and anomaly detection.

• Amazon Redshift – Facilitates real-time business intelligence and analytics.

• Google BigQuery – Supports instant analysis of streaming data, such as social media activity.

• Cassandra – Manages high-throughput event streaming, commonly used in telecommunications.

• MongoDB – Synchronizes real-time content and transactional data for applications.

• Elasticsearch – Powers real-time search and sentiment analysis, frequently used in e-commerce and retail.

• StarRocks – A high-performance real-time OLAP database that enables sub-second query responses on Kafka streaming data, making it ideal for real-time analytics and business intelligence applications.

By serving as a scalable data backbone, Kafka enables organizations to build robust, real-time data pipelines that can handle continuous data ingestion and processing efficiently.

How Kafka Scales Efficiently

One of Kafka’s greatest strengths is its ability to scale horizontally. Unlike traditional databases or message queues that may struggle under increasing loads, Kafka can expand seamlessly by adding more brokers and partitions. This ensures that as data volumes grow, Kafka remains performant without causing disruptions.

How Kafka Achieves Scalability:

• Partitioning Topics: Kafka divides topics into multiple partitions, allowing different consumers to process data in parallel.

• Load Balancing via Consumer Groups: Consumers within a group share the workload, ensuring efficient distribution.

• Replication for Fault Tolerance: Partitions are replicated across brokers to prevent data loss in case of failures.

• Efficient Data Storage: Kafka uses an append-only log structure, optimizing sequential writes for efficient disk I/O, but it also supports log segment deletion and compaction depending on retention policies.

This architecture enables Kafka to handle millions of messages per second while maintaining low latency, making it ideal for mission-critical applications.

A Real-World Example: How Netflix Uses Kafka

Netflix, one of the world’s largest streaming services, processes hundreds of billions of events daily using Kafka. These events power various aspects of their platform, including:

• Personalized Recommendations: By analyzing user interactions in real-time, Netflix suggests relevant content almost instantly.

• Seamless Streaming Experience: Kafka helps monitor network conditions and optimize video playback quality based on user bandwidth.

• Logging and Debugging: Netflix collects vast amounts of operational data, allowing engineers to quickly detect and fix service disruptions.

By using Kafka as a real-time event pipeline, Netflix ensures that millions of users receive smooth streaming experiences and personalized recommendations, all while maintaining system reliability and performance.

Strengths and Weaknesses of Kafka

Advantages of Kafka

Scalability and Performance

Kafka’s distributed architecture enables horizontal scalability, allowing businesses to expand capacity by adding more brokers as workloads increase. The ability to partition topics ensures data processing occurs in parallel, significantly improving efficiency.

Benchmarking tests highlight Kafka’s impressive performance. It can achieve high throughput (hundreds of MB/s) with low latency (~5ms in optimized environments), but actual performance depends on hardware, configuration, and workload.. These metrics demonstrate how Kafka handles high data volumes while maintaining low latency. Additionally, Kafka’s efficient use of hardware resources reduces infrastructure costs by 30-40% compared to alternatives. Businesses have also reported a 30x improvement in analytics performance when using Kafka.

This scalability and performance make Kafka ideal for applications requiring high-speed data streaming, such as financial transactions or IoT sensor data.

Durability and Fault Tolerance

Kafka ensures data durability and fault tolerance through several mechanisms. Each partition is replicated across multiple brokers, so your data remains accessible even if one broker fails. Kafka’s leader election process promotes an in-sync follower to leader status when a failure occurs, maintaining system availability.

You can also configure acknowledgment settings to balance performance and reliability. For example, setting acks=all ensures that all replicas confirm data writes, providing maximum durability. These features make Kafka a reliable choice for critical systems where data loss is unacceptable.

Flexibility and Versatility

Kafka’s event-driven architecture enables it to adapt to diverse use cases across industries. In IoT, it streams real-time data from sensors, such as windmills monitoring environmental conditions. Financial institutions use Kafka for fraud detection and risk management. In retail, it powers order processing and customer relationship management.

Kafka also supports stream processing, allowing you to transform and analyze data in motion. This capability is essential for applications like network monitoring in telecom or real-time communication in gaming platforms. Its ability to integrate with various tools and systems further enhances its versatility.

Whether you need to process healthcare data or enable real-time analytics, Kafka’s flexibility makes it a valuable tool for modern businesses.

Open Source and Community Support

Kafka’s open-source model means businesses can use and customize it freely. It benefits from continuous innovation driven by a global developer community. Thousands of contributors actively enhance Kafka, and resources like documentation, tutorials, GitHub repositories, and forums make troubleshooting easier.

Additionally, Kafka's ecosystem includes Kafka Connect for integrating with external systems and Kafka Streams for real-time processing, ensuring that Kafka remains a flexible tool for modern event-driven architectures.

Kafka Disadvantages

Complexity in Setup and Maintenance

Managing a Kafka cluster requires expertise in distributed systems, networking, and security. The architecture—comprising brokers, topics, partitions, and replication—introduces a learning curve, which can be challenging for teams unfamiliar with streaming platforms.

Kafka Connect, while useful, requires additional effort for configuring and managing connectors. Native monitoring tools may also fall short, requiring third-party solutions to ensure performance tracking and troubleshooting.

Resource-Intensive Nature

Kafka’s operational requirements are significant, demanding high-performance hardware:

Storage is also a key factor. A typical setup might require 12 x 1TB disks in RAID 10 for Kafka brokers, with high-throughput workloads necessitating 60GB of RAM for OS page caching. As producers and consumers scale, so do infrastructure costs, which may be prohibitive for smaller teams.

Message Retention Limitations

Kafka does not store messages indefinitely unless configured. Many users offload historical data to object storage, data lakes, or OLAP databases (e.g., StarRocks) for long-term storage and analytics. 

Regulatory compliance also presents challenges, as Kafka is not inherently designed for long-term archival storage. Businesses dealing with strict audit requirements may need complementary storage solutions, such as object storage, data lakes, or OLAP databases like StarRocks.

Situations Where Kafka May Not Be Ideal

Despite Kafka’s strengths, there are cases where it may not be the best solution:

• Low-Volume Workloads: If an application only processes a few thousand messages per day, Kafka’s distributed architecture may be overkill. RabbitMQ or ActiveMQ are better suited for lightweight workloads.

• Latency-Sensitive Workloads with Delays: Kafka prioritizes speed and consistency, which may not align with systems requiring intentional message delays or variable processing intervals.

• Legacy System Compatibility: Kafka may require substantial custom development and configuration to integrate with older, monolithic architectures.

• Simpler ETL Pipelines: Kafka-based streaming ETL can be complex to maintain. If batch processing suffices, alternatives like AWS Data Pipeline or Google Cloud Dataflow may be more practical.

By carefully assessing Kafka’s advantages and limitations, businesses can determine whether it is the right fit for their needs or if alternative solutions may provide a better balance of performance and complexity.

Comparison of Kafka Alternatives

While Kafka is a strong contender for real-time data streaming, alternative solutions exist, each with distinct strengths and weaknesses.

Technical Comparison

Common Use Cases for Apache Kafka

Apache Kafka is a cornerstone of modern event-driven architectures, offering real-time data streaming, event sourcing, data integration, and analytics. Its ability to handle high-throughput workloads with low latency makes it indispensable for various industries. Below are some of its most common use cases.

Real-Time Data Streaming

Kafka is designed to process and deliver large-scale event streams with minimal delay. Its high-throughput, fault-tolerant architecture ensures that businesses can react instantly to incoming data.

• Telecommunications: ISPs use Kafka to track customer activations in real time, optimizing service delivery.

• Finance: Institutions like Goldman Sachs leverage Kafka for real-time analytics on market data, enabling faster trading decisions.

• Media & Entertainment: Netflix processes hundreds of billions of messages daily, using Kafka to power seamless data collection and enhance user experience.

These examples highlight Kafka’s role in maintaining business agility and ensuring organizations can make real-time, data-driven decisions.

Event Sourcing and Log Aggregation

Kafka’s append-only log structure makes it a natural choice for event sourcing and log aggregation, ensuring consistency and scalability.

• Microservices Architecture: Kafka stores a persistent event history, allowing microservices to react to changes efficiently.

• Centralized Log Collection: Kafka streams logs from multiple sources, offering a unified and real-time view of application performance.

• Log Compaction: By retaining only the latest event for each key, Kafka reduces storage costs while preserving critical business data.

For organizations needing scalable, distributed event storage, Kafka provides a fault-tolerant and highly available solution.

Data Integration and ETL Pipelines

Kafka enables seamless data integration between multiple systems, supporting both real-time streaming and batch ETL processing.

• Database Synchronization: Change Data Capture (CDC) connectors allow for efficient extraction of real-time database updates.

• IoT Data Processing: Kafka ingests data from millions of sensors and devices, ensuring timely processing and analytics.

• Enterprise-Wide Data Pipelines: Organizations use Kafka to unify data across business units, enhancing real-time reporting and analysis.

With Kafka Streams and Kafka Connect, businesses can build scalable, fault-tolerant data pipelines that adapt to dynamic workloads.

Analytics and Monitoring

Kafka plays a critical role in real-time analytics and monitoring, helping organizations detect patterns, anomalies, and trends as they emerge.

• Stock Market Surveillance: Financial institutions use Kafka to track market fluctuations and fraud detection in real time.

• System Health Monitoring: Kafka collects application and server logs, providing instant insights into performance and potential failures.

• Telecom Service Optimization: ISPs monitor regional service activations to fine-tune network performance and detect outages.

By leveraging Kafka’s ability to process vast amounts of data with low latency, businesses gain the agility needed for proactive decision-making.

Is Apache Kafka Right for You?

When to Choose Apache Kafka

Apache Kafka is the right choice when your project demands high-performance data pipelines and real-time processing. Its distributed architecture handles massive data volumes with low latency, making it ideal for data-driven applications. If your workload involves millions of messages per second, Kafka’s scalability ensures smooth operations. Industries like finance, retail, and telecommunications rely on Kafka for its ability to process streaming data efficiently.

You should also consider Kafka if your use case requires fault tolerance and durability. Kafka’s replication mechanism ensures data remains accessible even during failures. For example, businesses that need reliable event logging or real-time analytics benefit from Kafka’s robust design. Additionally, Kafka’s flexibility allows you to integrate it with various tools, making it suitable for complex ecosystems.

When to Consider Alternatives

Kafka is not always the best fit. Simpler alternatives may be better suited for some workloads.

Choosing the right solution depends on data volume, latency requirements, and infrastructure complexity.

Key Questions to Evaluate Your Needs

Before adopting Kafka, consider the following:

• What are my data throughput and retention requirements?

• Does my team have experience with distributed systems and event-driven architectures?

• How will Kafka integrate with my existing tech stack?

• Do I need built-in fault tolerance and replication?

By assessing these factors, you can determine whether Kafka’s architecture aligns with your business needs.

FAQ

What is the difference between Kafka and traditional message queues?

Kafka and traditional message queues (e.g., RabbitMQ, ActiveMQ, Amazon SQS) serve different purposes.

• Kafka is optimized for high-throughput, real-time data streaming, enabling event-driven architectures. It stores messages for a configurable duration (even after consumption), allowing multiple consumers to process data independently and replay past messages.
• Traditional message queues (such as RabbitMQ) focus on message delivery guarantees (e.g., at-least-once or exactly-once delivery). These systems typically remove messages after they are successfully consumed, making them better suited for task queues, RPC, and transactional processing.
• Kafka supports exactly-once semantics (EOS) with idempotent producers and transactional processing, making it viable for transactional workloads. However, traditional message queues (e.g., RabbitMQ) may still be simpler for strict ordering and request-response patterns.

Do you need ZooKeeper to run Kafka?

• Older Kafka versions (pre-3.3) require Apache ZooKeeper for managing metadata, leader election, and cluster coordination.
• Newer Kafka versions (3.3 and later) support KRaft (Kafka Raft), which removes the dependency on ZooKeeper by integrating metadata management directly into Kafka.
• While KRaft eliminates the need for ZooKeeper, it introduces KRaft controllers (dedicated nodes). Organizations must plan for controller quorum management, similar to ZooKeeper’s role.
• However, as of 2024, KRaft is still stabilizing, and some organizations continue using ZooKeeper for production workloads.

Can Kafka handle small-scale workloads?

• Kafka excels in high-throughput, distributed data processing but can be overkill for small-scale workloads due to its complexity and infrastructure requirements.
• Kafka’s distributed architecture adds operational overhead (managing brokers, partitions, and replication).
• For low-volume messaging needs, lightweight alternatives such as RabbitMQ, NATS, or Redis Streams are more efficient.
• However, if small workloads are expected to grow, a managed Kafka service (e.g., Confluent Cloud, AWS MSK) can reduce operational complexity.

How does Kafka ensure data durability?

• Kafka achieves data durability through replication and configurable acknowledgment settings:
  • Replication: Each partition is replicated across multiple brokers (replication.factor > 1 ensures fault tolerance).
  • Leader Election: If a broker fails, Kafka automatically promotes an in-sync replica to leader status.
  • Acknowledgments (acks):
    • acks=0 (no acknowledgment) – fastest but least durable.
    • acks=1 (leader acknowledgment) – balances speed and durability.
    • acks=all (waits for all replicas to acknowledge) – ensures maximum durability.
• Compacted topics ensure long-term persistence of critical data.

Is Kafka suitable for beginners?

• Kafka has a steep learning curve due to its distributed architecture, partitioning model, and operational complexity.
• Beginners must understand concepts like brokers, topics, partitions, and consumer groups before effectively using Kafka.
• However, Kafka has extensive documentation, a large developer community, and managed cloud services that simplify adoption.
• For new users, starting with a managed Kafka service (e.g., Confluent Cloud, AWS MSK, Azure Event Hubs) reduces setup complexity and operational overhead.