A Comprehensive Comparison of Kafka and Kubernetes

Kafka requires a low latency network and high bandwidth, with storage providing fast, random access. The cluster also needs to be able to tolerate the failure of individual brokers.

Purchasing managed Kafka solutions from some providers provides a 24×7, fully managed service, freeing DevOps to focus on developing applications. These solutions often include cloud hosting.


Using Kafka vs Kubernetes allows for rapid deployment, scalability, and resilience of real-time data pipelines. Combining these two systems eliminates the need for orchestration, reducing the complexity of managing the cluster and allowing developers to focus on what matters most: building applications.

With Kafka, a topic is divided into multiple partitions to achieve scalability and parallelism. Each partition contains a linearly ordered, immutable sequence of records. Partitions can be accessed in different availability zones to provide fault tolerance. Kafka offers replication of a topic and mirroring between clusters to ensure the data is always available in case one broker goes down.

When deploying a Kafka cluster, it is important to consider the performance requirements of your application. The number of brokers should be sufficient to meet throughput requirements without exceeding capacity while ensuring that the brokers have enough resources for failure handling and a high availability zone.

A good way to monitor Kafka performance is to use a tool, which provides synthetic monitoring for complex, distributed systems and helps DevOps teams find performance issues before they impact the end-user experience. Using Datadog, you can proactively visualize performance metrics and correlate data from your infrastructure, Kubernetes containers, and Kafka cluster.


Many Kafka solutions offer features to help ensure data is durable and available by replicating across nodes and data centers. Some also provide a wide range of other functionality that can be installed to easily integrate with other data systems, transform data, and build real-time applications. However, these solutions come at a cost. In addition to the software license costs, additional expenses are associated with deploying and administering these systems. These include hardware and cloud costs as well as the cost of staff time to set up and administer these complex environments.

As a distributed event streaming platform, Kafka enables decoupling between data producers and consumers. This allows multiple applications to consume data streams from a central broker simultaneously. Kafka organizes records into logical channels called topics and stores them in partitions. Data producers publish records on these topics and can specify the partition where they want the record to be stored. Consumers subscribe to these topics and can read messages from any partitions. Consumer groups allow competing consumers to divide topic partitions equally, which resembles traditional message queues but scales horizontally to handle massive amounts of data.

Typically, Kafka brokers can be deployed as pods in a Kubernetes cluster. This makes it easy to recover them if a node or container fails since the system automatically restarts pods and containers. While the advantage of a containerized deployment is that it is easier to move around, from peak performance and reliable management perspective, it’s better to deploy Kafka outside of k8s. This is because Kafka needs a file system cache, and projects like Virtlet still need to virtualize this.


The Kafka platform is designed to handle real-time data streams and offers several security measures. These include authentication, access control for operations, and encryption between brokers. These features can help protect against unauthorized access to the data, a common concern among enterprises.

The data stored in a Kafka cluster is replicated across multiple hosts to achieve fault tolerance. This also helps to ensure that a broker can be rescheduled on another host if it fails without losing any data. The data is then stored in an empty disk container called a PersistentVolume. This makes it possible for Kubernetes to use the PersistentVolume in a different availability zone or even on a different physical server.

In addition to these security features, Kafka can be configured with ACLs to allow or deny specific actions for certain users. This is important if you have topics that should be read from only specific clients or hosts to avoid data corruption and deserialization errors.

Another aspect of Kafka security is its ability to encrypt data in-flight using SSL/TLS. While this does add some overhead to the CPU and JVM execution, it can be critical to protect sensitive data. Encryption is also helpful if you need to ensure that communication between brokers is private.


Kafka carries a lot of information, so it’s important to keep an eye on the amount of network traffic it generates. Monitoring network throughput for Kafka brokers and clients can help you identify issues that can impact performance.

Using an open-source JMX-based tool like Jolokia or the New Relic Java Agent, you can retrieve key metrics on a per-broker and per-topic basis. These metrics can be compared with historical data to determine whether the cluster is functioning as expected. For example, if frequent consumer rebalance events are observed, it could indicate that the application is not connecting to the broker correctly or that the cluster needs more brokers to handle the load.

A Kafka cluster comprises a set of servers, called brokers, responsible for storing and serving data. Each topic is divided into partitions, each with a leader and followers that replicate the data to be accessed from multiple servers in case of a node failure. When a server isn’t handling requests quickly enough, it will notify the other brokers to take over the role so that the log stays intact.

Monitoring producer network throughput provides visibility into the number of messages sent to the brokers and can help you decide if it is necessary to increase the number of producers or change the configuration of existing ones. For example, suppose your application uses more than one Kafka server. In that case, you can configure them to connect to the same availability zone to reduce cross-AZ network traffic and improve performance.