Good data quality is one of the most critical requirements in decoupled architectures, like microservices or data mesh. Apache Kafka became the de facto standard for these architectures. But Kafka is a dumb broker that only stores byte arrays. The Schema Registry enforces message structures. This blog post looks at enhancements to leverage data contracts for policies and rules to enforce good data quality on field-level and advanced use cases like routing malicious messages to a dead letter queue.
Real-time data beats slow data in almost all use cases. But as essential is data consistency across all systems, including non-real-time legacy systems and modern request-response APIs. Apache Kafka’s most underestimated feature is the storage component based on the append-only commit log. It enables loose coupling for domain-driven design with microservices and independent data products in a data mesh. This blog post explores how Kafka enables data consistency with a real-world case study from financial services.
Data Streaming is one of the most relevant buzzwords in tech to build scalable real-time applications in the cloud and innovative business models. Do you wonder about my predicted TOP 5 data streaming trends in 2023 to set data in motion? Check out the following presentation and learn what role Apache Kafka plays. Learn about decentralized Data Mesh, cloud-native lakehouse, data sharing, improved user experience, and advanced data governance.