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Textbook in PDF format

The exponential growth of data combined with the need to derive real-time business value is a critical issue today. An event-driven data mesh can power real-time operational and analytical workloads, all from a single set of data product streams. With practical real-world examples, this book shows you how to successfully design and build an event-driven data mesh.
Data mesh is a fundamental shift in the way we think about, create, share, and use data. We promote data to a first-class citizen by carefully curating and crafting it into data products, supported with the same level of care and commitment as any other business product. Consumers can discover and select the data products they need for their own use cases, relying upon the commitment of the data product producer to maintain and support it. At its heart, data mesh is as much about technological reorganization as it is about the renegotiation of social contracts, responsibilities, and expectations.
The principles of the data communication layer were simple enough: treat data as a first-class citizen, make it reliable and trustworthy, and produce it through event streams so that you can power both operational and analytical applications. The beauty of data mesh is that it’s not a big-bang total revision of everything we know about data. In fact, it’s really an affirmation of best practices, both social and technical, based on the collective hard work and experiences of countless people. It provides the framework necessary to discuss how to go about creating, communicating, and using data, acting as a lingua franca for the data world.
Building an Event-Driven Data Mesh provides
Practical tips for iteratively building your own event-driven data mesh, including hurdles you'll experience, possible solutions, and how to obtain real value as soon as possible
Solutions to pitfalls you may encounter when moving your organization from monoliths to event-driven architectures
A clear understanding of how events relate to systems and other events in the same stream and across streams
A realistic look at event modeling options, such as fact, delta, and command type events, including how these choices will impact your data products
Best practices for handling events at scale, privacy, and regulatory compliance
Advice on asynchronous communication and handling eventual consistency