Apache Kafka Features – What Can & Can't Do

Apache Kafka is a powerful event streaming platform with a variety of use cases, but misconceptions about its capabilities abound. Misinformation can lead to significant challenges within your tech stack. 

If someone oversells Kafka's abilities, you might implement it only to find it doesn't meet your needs. Conversely, false information could lead you to overlook this versatile open-source system when it may be a perfect fit.

Let’s clarify what Apache Kafka can do — and what it can’t.

What Apache Kafka can do

Kafka is an event streaming platform developed with the programming languages Python and Scala. It can capture data in real-time, which makes it a popular solution among app developers and data scientists. It has a distributed architecture that can coordinate multiple Kafka clusters operating on various cloud servers, virtual machines, and other devices, known by users as “Kafka brokers.”

What can Kafka do?

• Process real-time messages: Apache Kafka excels at processing millions of messages in real time. Its distributed architecture ensures that Kafka is a highly fault-tolerant, low-latency messaging system. This performance often surpasses that of traditional messaging systems like Advanced Message Queuing Protocol (AMQP), Java Message Service (JMS), and RabbitMQ.
• Handle varied data volumes: Kafka can efficiently stream small and large data volumes with minimal data loss. Major companies like Netflix, LinkedIn, and Spotify rely on Kafka to stream data. Whether you’re dealing with big data for trend analysis or managing small service communications, Kafka can meet those demands.
• Meet distributed storage needs: Kafka's distributed system allows for true decoupling, effectively addressing data issues caused by back pressure (when data accumulates because a system can't process it quickly enough). This architecture enables prioritization of events, the ability to replay unprocessed messages, and support for various communication protocols.
• Stream ETL data integration:
  In a world where microservices are preferred over monolithic applications, Kafka enhances data mesh strategies by providing developers with better control over streaming data to ETL solutions. For advanced data structures, Kafka is often favored over low-code ETL platforms, particularly for scalable data pipelines.
• Support stateless and stateful data processing: Kafka employs both stateless and stateful operations to enhance stream processing speed and reliability. Stateless processing allows for independent messaging tasks, while stateful processing is suitable for more complex tasks, such as aggregating multiple data streams.

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While Kafka meets and exceeds expectations in these areas, it’s important to understand its limitations.

What Apache Kafka cannot do

Despite its strengths, Apache Kafka isn't a one-size-fits-all solution. Here are scenarios where Kafka may not be the best fit:

• Serve as a proxy for large client requests

Kafka isn’t designed to handle requests from millions of clients directly, such as those from a popular mobile app. However, Kafka-native proxies like REST and MQTT can be used effectively in these situations.

• Act as a full IoT platform 

While Kafka has its strengths, it’s not the ideal choice for IoT applications. Protocols like MQTT are better suited for IoT devices due to their support for bi-directional communication and low bandwidth requirements. For managing IoT devices, consider using OPC Unified Architecture (UA), which provides a pub-sub layer similar to Kafka but tailored for IoT applications.

• Function as an API management platform 

Many assume that Kafka Streams and Kafka Connect provide comprehensive API management, but that’s not the case. While Kafka supports API management, it doesn’t offer a complete solution. Dedicated API management tools are better suited for building production-ready connectors and managing APIs effectively.

• Fulfill complex queries and batch analytics 

Although Kafka handles real-time events efficiently, it’s not designed for complex data queries or extensive batch analytics. While Kafka can manage transactional queries and simple aggregations, larger, more intricate tasks require specialized solutions.

• Process hard real-time data

Kafka is adept at processing large amounts of data quickly, but it shouldn’t be confused with embedded systems designed for safety-critical applications. Kafka cannot match the speed or reliability of dedicated embedded software, which is essential for managing sensitive information in real-time.

Should your company use Kafka?

Apache Kafka has numerous strengths, but it isn’t the right solution for every use case. If you’re unsure whether Kafka is suitable for your needs, don’t hesitate to reach out to Adservio. We can help you determine if Apache Kafka is the right fit for your organization’s data streaming and integration requirements.