Data Streaming is not a race, it is a journey! Event-driven architectures and technologies like Apache Kafka or Apache Flink require a mind shift in architecting, developing, deploying, and monitoring applications. This blog post explores success stories from data streaming journeys across industries, including banking, retail, insurance, manufacturing, healthcare, energy & utilities, and software companies.
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.
The concepts and architectures of a data warehouse, a data lake, and data streaming are complementary to solving business problems. Unfortunately, the underlying technologies are often misunderstood, overused for monolithic and inflexible architectures, and pitched for wrong use cases by vendors. Let’s explore this dilemma in a blog series. This is part 4: Case Studies for cloud-native data streaming and data warehouses.