Building an Event-Driven Data Mesh: Patterns for Designing & Building Event-Driven Architectures - Tapa blanda

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Adam Bellemare is a Staff Technologist, Office of the CTO at Confluent. Previously, staff Engineer, Data Platform at Shopify and he was at Flipp from 2014, first as a Senior Developer, followed by a role as Staff. He has also held positions in embedded software development and quality assurance. His expertise includes: Devops (Kafka, Spark, Mesos, Zookeeper Clusters. Programmatic Building, scaling, destroying); Technical Leadership (Bringing Avro formatting to our data end-to-end, championing Kafka as the event-driven microservice bus, prototyping JRuby, Scala and Java Kafka clients and focusing on removing technical impediments to allow for product delivery); Software Development (Building microservices in Java and Scala using Spark and Kafka libraries); and Data Engineering (Reshaping the way that behavioral data is collected from user devices and shared with our Machine Learning, Billing and Analytics teams). He is the author of Building Event-Driven Microservices (2020) with O'Reilly

De la contraportada

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.

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