Building a production-grade stream processing platform with Apache Flink requires more than just the right technology stack, it also requires addressing complex operational and scalability challenges.

The foundation lies in utilizing open-source systems like Apache Flink, Apache Kafka, and Debezium, that are purpose-built for tackling stream processing challenges. Together, they provide the systems to transform and analyze data streams, to ingest, store, and transport those streams, and to support change data capture (CDC) for the continuous tracking of updates and changes.

This guide will help you understand what building a production-ready stream processing platform entails, what companies should consider beyond the core technologies, and what is needed to turn promised capabilities into actual line-of-business functionality.

Download this guide to explore:

• Data connections and schema evolution: Getting data into and out of your stream processing platform is where it all begins, and there are key considerations for making this happen, including translating data types across connected systems and managing schema changes.

• Meeting the needs of developers: Whether the language of choice is SQL, Java, or Python, developers need to be able to focus on the business logic for their data pipelines while using the SDLC tools they depend on, like Git, CI/CD automation, and unit testing.
• Scalability and day-2 operations: Production systems don’t run on their own, so it’s critical to provide the necessary security, compliance, observability, and on-going support when building a managed Flink service.