This chapter covers
- Integrating Kafka within a data mesh architecture
- Real-time data processing and event-driven architectures
- Common anti-patterns in Kafka implementation
In this chapter, we’ll explore some use cases in which Kafka excels, showcasing its versatility in industrial applications, data integration, and analytics. From manufacturing to pharmaceuticals, Kafka’s ability to process vast amounts of data in real time enables businesses to make informed decisions, optimize processes, and enhance customer experiences.
It’s equally important to recognize the limitations of Kafka. While it’s a powerful tool, it’s not a universal solution for every data challenge. Misapplication can lead to inefficiencies and complications that undermine its intended benefits. Therefore, we’ll also highlight scenarios where Kafka may not be the best choice, addressing common misconceptions and providing guidance on when to consider alternative solutions. By understanding both the strengths and limitations of Kafka, organizations can strategically deploy this technology to create robust and efficient data systems that meet their unique needs.
18.1 Kafka as the core of a data mesh
In recent years, concepts for data management within companies have evolved. Just as the term microservices became popular in software development, the term data mesh has emerged in the data world. But what exactly does it mean?