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.
Digital transformation requires agility and fast time to market as critical factors for success in any enterprise. The decentralization with a data mesh separates applications and business units into independent domains. Data sharing in real-time with data streaming helps to provide information in the proper context to the correct application at the right time. This blog post explores a case study from the financial services sector where a data mesh was built across countries for loosely coupled data sharing but standardized enterprise-wide data governance.
Data Mesh is a new architecture paradigm that gets a lot of buzzes these days. This blog post looks into this principle deeper to explore why no single technology is the perfect fit to build a Data Mesh. Examples show why an open and scalable decentralized real-time platform like Apache Kafka is often the heart of the Data Mesh infrastructure, complemented by many other data platforms to solve business problems.