The combination of Apache Kafka and Kubernetes helps developers create EDAs and real-time data pipelines that stream and integrate data between applications.
Multiple drivers are leading businesses to modernize applications and move the development and deployment of new applications to cloud-native approaches. At the same time, there is an increased need to make use of events data for those applications to improve the speed and responsiveness of their internal and customer-facing processes. What’s needed to make the transition and embrace events data is a combination of application cloud services and event-driven architectures (EDAs).
Critical elements of EDAs: Kubernetes, service meshes, and Kafka
Modern applications must ingest and process large amounts of events data, and they also need flexibility. They must accommodate new datasets, frequent new feature enhancements, and changing analytics requirements without disrupting operations. As a result, most EDA-based applications are based on loosely coupled microservices that use APIs and middleware to run in hybrid cloud environments.
While event-driven architectures and reactive programming models have been employed in the past, the move to cloud-native architectures with microservices and container-based workloads is making them more practical and provides many benefits. For example, cloud-native solutions are inherently scalable and resilient. An event-driven architecture can leverage these traits to ensure agility and responsiveness.
At the heart of many EDA-based and container-based applications is cloud services. One example is the use of Kubernetes, the open-source platform to automate the deployment and management of containerized applications. Kubernetes provides service discovery and load balancing, storage orchestration, self-healing, automated rollouts and rollbacks, and more.
Kubernetes clusters can span hosts across on-premises, public, private, or hybrid clouds. For this reason, Kubernetes is an ideal platform for hosting cloud-native applications that require rapid scaling, like real-time data streaming.
Besides Kubernetes, a microservices architecture requires a service mesh for different microservice instances to communicate with each other and work together. A service mesh is essentially an infrastructure layer that lets businesses connect, secure, control, and observe their microservices. The service mesh also lets businesses configure how service instances perform critical actions such as service discovery, load balancing, data encryption, and authentication and authorization.
Today, a leading choice for a service mesh is Istio. It was developed in a collaboration between Google, IBM, and Lyft. Istio is an open-source service mesh that lets businesses connect, monitor, and secure microservices deployed on-premises, in the cloud, or with orchestration platforms like Kubernetes.
The last critical element of EDA-based modern applications is Apache Kafka. Kafka is a distributed data streaming platform that can publish, subscribe to, store, and process streams of records in real time. It is designed to handle data streams from multiple sources and deliver them to multiple elements that need the data for analysis.
Kafka is built into streaming data pipelines that share data between systems or applications, and it is also built into the systems and applications that consume that data. Kafka supports a range of use cases where high throughput and scalability are vital. Since Kafka minimizes the need for point-to-point integrations for data sharing in certain applications, it can reduce latency to milliseconds. Data is available to users faster, which can be advantageous in cases that require real-time data availability, such as IT operations and e-commerce.
See also: On-Ramp to EDA: Basics and Benefits
How Kubernetes scales Kafka
Developers need a scalable platform to host Kafka applications, and Kubernetes is the answer. By combining Kafka and Kubernetes, businesses gain all the benefits of Kafka and also the advantages of Kubernetes, which include scalability, high availability, portability, and easy deployment.
The scalability of Kubernetes is a natural complement to Kafka. In Kubernetes, businesses can scale resources up and down with a simple command or scale automatically based on usage as needed to make the best use of computing, networking, and storage infrastructure. Such a capability enables Kafka to share a limited pool of resources with other applications. Kubernetes also offers Kafka portability across infrastructure providers and operating systems. With Kubernetes, Kafka clusters can span across on-site and public, private, or hybrid clouds and use different operating systems.
Many companies do not have extensive expertise in EDAs, Kubernetes, or Kafka. Most turn to partners that bring years of experience and best practices. Enter Red Hat.
Red Hat offers Red Hat OpenShift, an enterprise Kubernetes application platform. Red Hat OpenShift brings an enterprise-grade Kubernetes distribution with hundreds of security, defect, and performance fixes in each release. It is a single, integrated platform for operations and development teams. Red Hat OpenShift includes an enterprise-grade Linux operating system, container runtime, networking, monitoring, registry, and authentication and authorization solutions. The software helps automate lifecycle management to get increased security, tailored operations solutions, easy-to-manage cluster operations, and application portability.
For Kafka help, Red Hat AMQ Streams offers Kafka on the Red Hat OpenShift Container Platform. AMQ Streams is a scalable, distributed, and high-performance data streaming capability based on Kafka, running on Kubernetes. Specifically, AMQ Streams simplifies the deployment, configuration, management, and use of Kafka on OpenShift, enabling the inherent benefits of OpenShift, such as elastic scaling.
See also: Why Event-Driven is More Than Just Kafka
A final word
As businesses move to be more reactive to customer and market changes, events data and EDAs will play a significant role. A key factor in the successful use of EDAs is a move to applications based on loosely coupled microservices, containers, and a highly-scalable event processing engine.
Kubernetes and Kafka bring the needed help with managing containers and the streaming data found in most businesses today. Both have large open-source communities that drive continuous innovations and ensure rapid changes when problems occur or new features and capabilities are needed.
Bottom line: The combination of Kafka and Kubernetes helps developers to create event-driven, real-time data pipelines that stream and integrate data between applications.
