Kin and Carta : Getting Started with Spring Cloud Data Flow and Confluent Cloud

Data is the currency of competitive advantage in today's digital age. All organizations struggle with their data due to the sheer variety of data types and ways that it can be shaped, packaged, and evaluated.

Within organizations, teams use different tools, fragmented rule sets, and multiple sources to find value within the data. These operational differences lead to divergent definitions of data and a siloed understanding of the ecosystem.

These challenges have led to the rise of several new technologies, including Apache Kafka® and Spring Cloud Data Flow. These help transform data ownership responsibilities and, at the same time, prepare them for the transition from batch to real-time data processing. Drawing insights as data is created versus looking at it as a past event provides a critical view into the operation of your business at many levels. Event streaming enables you to perform everything from responding to inventory issues, to learning about business issues before they become issues.

This blog post gives you the foundation for event streaming and designing and implementing real-time patterns. Using Confluent Schema Registry, ksqlDB, and fully managed Apache Kafka as a service, you can experience clean, seamless integrations with your existing cloud provider.

What follows is a step-by-step tutorial of how to use these tools and lessons learned along the way. Follow this walkthrough to configure Confluent Cloud and Spring Cloud Data Flow for development, implementation, and deployment of cloud-native data processing applications.

By the end of this tutorial, you should have the knowledge and tools to set up Confluent Cloud and Spring Cloud Data Flow and understand the power of event-based processing in the enterprise landscape. The tutorial also reviews the basics of event stream development and breaks down monolithic data processing programs into bite-size components.