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Red Hat OpenShift Streams for Apache Kafka
Red Hat® OpenShift® Streams for Apache Kafka is a cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems.
Deliver real-time experiences and connected microservices
Red Hat OpenShift Streams for Apache Kafka makes it easy to create, discover, and connect to real-time data streams no matter where they are deployed. Streams are a key component for delivering event-driven and data analytics applications. The combination of seamless operations across distributed microservices, large data transfer volumes, and managed operations allows teams to focus on team strengths, speed up time to value, and lower operational costs.
Apache Kafka allows organizations to modernize existing systems and deliver real-time experiences to employees, customers and partners. Red Hat OpenShift Streams for Apache Kafka goes farther than core Kafka technology because of what comes packaged alongside it: OpenShift Streams for Apache Kafka includes a Kafka ecosystem and is part of a family of cloud services—and the Red Hat OpenShift product family—which helps you build a wide range of data-driven solutions.
Benefits of using OpenShift Streams for Apache Kafka
- Faster application velocity allows teams to begin developing immediately, continuously modify individual microservices, and implement new technology quickly.
- Reduced complexity allows teams to provide scalable, reliable and always-on Apache Kafka service, saving time and resources.
- A curated Kafka ecosystem for building, deploying and scaling applications—including platform, application, and data services—to simplify the delivery of stream-based applications in public and private clouds.
Many applications still rely on batch data, which can lead to frustration for customers and delayed results. The use of streaming data eliminates the reliance on batch data to enable better, more immediate digital experiences.
When designing hybrid cloud applications, data streams provide the backbone for capturing, communicating, and processing events for event-driven, distributed application architectures.
Developers strive to expose functionality as microservices as they allow teams to stay independent. Using streams to communicate between microservices preserves loose coupling and allows teams to remain agile.