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Architectural Patterns

Architectural Patterns

By : Murali, Pethuru Raj, J, Pethuru Raj Chelliah
2.4 (5)
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Architectural Patterns

Architectural Patterns

2.4 (5)
By: Murali, Pethuru Raj, J, Pethuru Raj Chelliah

Overview of this book

Enterprise Architecture (EA) is typically an aggregate of the business, application, data, and infrastructure architectures of any forward-looking enterprise. Due to constant changes and rising complexities in the business and technology landscapes, producing sophisticated architectures is on the rise. Architectural patterns are gaining a lot of attention these days. The book is divided in three modules. You'll learn about the patterns associated with object-oriented, component-based, client-server, and cloud architectures. The second module covers Enterprise Application Integration (EAI) patterns and how they are architected using various tools and patterns. You will come across patterns for Service-Oriented Architecture (SOA), Event-Driven Architecture (EDA), Resource-Oriented Architecture (ROA), big data analytics architecture, and Microservices Architecture (MSA). The final module talks about advanced topics such as Docker containers, high performance, and reliable application architectures. The key takeaways include understanding what architectures are, why they're used, and how and where architecture, design, and integration patterns are being leveraged to build better and bigger systems.
Table of Contents (13 chapters)
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Event sourcing pattern

Event sourcing is an architectural pattern in which the state of an application is determined by a sequence of events. Each event in the sequence is recorded in an append-only event store or stream. Conventionally, most software applications work with data and the application has to maintain the current state of the data by updating it as users work with the data. A typical data process is to read data from the store, make some modifications to it, and update the current state of the data with the new values. The transaction is one that changes the data value. However, this way of data update and keeping up the data consistency has many inherent limitations. It requires a two-phase commit (2PC) when accomplishing distributed transactions. Any 2PC commit reduces the throughput of transactions substantially. When there are many concurrent users, there is a...

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