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AI-Enabled Event-Driven Architectures Building Intelligent Enterprise Cloud Systems
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- FormatePub
- ISBN8235385160
- EAN9798235385160
- Date de parution09/08/2026
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIoakim Ioakim
Résumé
AI-Enabled Event-Driven Architectures: Building Intelligent Enterprise Cloud SystemsSarath Kumar Kallayil Sreedharan Enterprise systems have spent three decades built on a single premise: a caller asks a question and waits for an answer. Multiplied across hundreds of services, that premise produces a distributed monolith - all the operational complexity of microservices, none of the independence. This book argues for the inversion.
A service states a fact rather than asking a question, and that fact is durable, ordered, and replayable by whoever cares about it. What is new is that the same stream of facts turns out to be exactly what machine learning systems need: once an organisation has a governed real-time event backbone, adding intelligence stops being a separate data-engineering programme and becomes an extension of infrastructure that already exists.
Across sixteen chapters, the book moves from foundations to autonomy - the anatomy of an event, layered reference architectures, event meshes spanning clouds and regions, streaming inference and training-serving skew, event sourcing with CQRS, consistency across service boundaries, security and governance, observability, agentic control planes, and a staged adoption roadmap - closing with performance engineering, six worked deployments, data quality, and platform engineering.
Chapter 4 stands apart, presenting the author's original AERIS and SMART frameworks, clearly marked as proposals for evaluation rather than established practice. Deliberately not a tutorial for any particular broker, it offers instead the reasoning that should precede a vendor choice: the trade-offs that persist regardless of which logo is on the cluster. Written for architects, senior engineers, and technology leaders - including those weighing whether the answer should be no.
A service states a fact rather than asking a question, and that fact is durable, ordered, and replayable by whoever cares about it. What is new is that the same stream of facts turns out to be exactly what machine learning systems need: once an organisation has a governed real-time event backbone, adding intelligence stops being a separate data-engineering programme and becomes an extension of infrastructure that already exists.
Across sixteen chapters, the book moves from foundations to autonomy - the anatomy of an event, layered reference architectures, event meshes spanning clouds and regions, streaming inference and training-serving skew, event sourcing with CQRS, consistency across service boundaries, security and governance, observability, agentic control planes, and a staged adoption roadmap - closing with performance engineering, six worked deployments, data quality, and platform engineering.
Chapter 4 stands apart, presenting the author's original AERIS and SMART frameworks, clearly marked as proposals for evaluation rather than established practice. Deliberately not a tutorial for any particular broker, it offers instead the reasoning that should precede a vendor choice: the trade-offs that persist regardless of which logo is on the cluster. Written for architects, senior engineers, and technology leaders - including those weighing whether the answer should be no.