Building Real-Time Streaming Applications With Apache Kafka on Kubernetes

Streaming applications are online services that generate data streams based on various events. Whether they are websites, online games, or connected devices, these applications drive the growth of the Internet of Things (IoT).

Kafka enables developers to store, process and integrate large volumes of real-time event streams. It provides guaranteed ordering, zero message loss, and efficient exactly-once processing for mission-critical use cases.


Kafka is a horizontally scalable, cloud-native messaging and streaming platform that enables unified data pipelines for real-time event processing. It supports publish-and-subscribe messaging, event streaming, and distributed state storage.

The Kafka ecosystem includes multiple Apache technologies that you can use to build real-time streaming applications, event-driven architectures, and big data analytics solutions. They have Apache Flink, an engine that ingests streams, performs operations on them in real time, and publishes the results to Kafka or another application.

Many organizations also use Apache NiFi, a data flow management system that can run as a Kafka producer and a Kafka consumer to manage data flows. It has a visual interface and can run in multiple languages, including Java.

Streaming data involves sending and receiving information in real time from different sources such as IoT devices, mobile phones, databases, setup boxes, cloud services, and other data sources. Using Kafka to send and receive real-time data can help companies reduce the latency and response times for critical systems like Internet of Things (IoT) monitoring and alerting, financial services, and gaming.

Kafka stores data in a cluster of brokers (servers) that distribute data into user-configurable categories called topics, then into smaller subsets called partitions replicated and distributed for fault tolerance. It then provides APIs for users to manage Kafka clusters, brokers, topics, and partitions.


Building real-time streaming applications with Apache Kafka on Kubernetes is a great way to scale out a cluster without relying on external load balancers. This is an essential feature for real-time data applications that depend on low latency.

Another essential benefit of deploying and managing a Kafka cluster with Kubernetes is that it makes it much easier to perform everyday operational tasks. For example, rolling cluster updates and upgrades, scaling brokers, moving brokers, and monitoring are much more accessible.

Kubernetes also includes built-in features for handling failures and ensuring the high availability of Kafka. For example, it automatically reschedules failed Kafka broker containers and supports rolling updates without downtime, thereby enhancing the reliability of a Kafka deployment.

Kafka on Kubernetes lets you quickly build data streaming and event-driven architectures, including ETL pipelines that move data from a source to a sink. For example, an ETL pipeline may ingest data from a database or cloud storage and then send it to a processing cluster.

Then, the processing cluster can run analytics on the data and create an end-to-end stream of events that can be used for other applications. These processes, commonly known as an ETL pipeline, can help businesses transform data into business-ready information to drive better decisions.

Several companies use Kafka as part of their big-data infrastructures


Availability is one of the most important metrics to consider when measuring resiliency. An application can function properly regardless of how much time it spends on downtime. Understanding the underlying factors determining availability is critical to ensuring your system’s reliability and ensuring you have the resources necessary to maintain its capacity over time.

Kafka uses clusters of servers (called brokers) to provide high availability and fault tolerance for real-time data streaming applications. Brokers store messages in topics, split them into partitions, and allow consumers to read statements by subject, section, and offset.

Developers can use Kafka to track high-throughput activity like website traffic, ingest data from IoT sensors, keep tabs on shipments, monitor hospital patients, and so much more. They can also implement stream processing logic to apply transformations and group events around specific periods for analysis.

In addition to a robust, scalable architecture, Kafka is built for durability, with all published messages stored for a configurable amount of time before being deleted or replayed. This makes it an excellent choice for storing data that needs to be kept for historical analysis or disaster recovery purposes.

Once you’ve mastered deploying a Kafka cluster on Kubernetes, you can begin exploring more complex use cases that leverage stream processing to drive real-time business operations and decisions. Start by learning to connect Kafka with a broader range of systems, applications, and databases using the platform’s connectors. Then, build end-to-end pipelines to transform and prepare your data for use by streaming applications.


Kafka is an excellent platform for building real-time streaming applications. Its architecture is based on publish-subscribe messaging and a full queue. This makes it easy to use for large-scale applications.

Streaming data is valuable for many industries, including financial services and healthcare. The data is a critical asset for these organizations, enabling them to provide personalized and targeted content to their customers.

This is why many organizations are using Apache Kafka to build real-time streaming applications. In addition to allowing developers to develop and deploy these applications quickly, Kafka has a wide range of features that make it highly scalable and flexible.

For example, the platform offers a variety of capabilities for analyzing and processing streams in real time. It also provides a variety of data formats, which is essential for organizations that need to work with various types of data.

Kubernetes enables organizations to automate upgrades, scaling, restarts, and monitoring operations. With these capabilities, organizations can efficiently run their Kafka clusters on the platform without switching to a different infrastructure.

Another reason to consider Kafka on Kubernetes is that the platform offers various performance optimizations. For example, Kubernetes can automatically rebuild brokers after they fail, which has a lower I / O cost than rebuilding the broker from scratch.


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