Book Image

Architectural Patterns

By : Anupama Murali, Harihara Subramanian J, Pethuru Raj Chelliah
Book Image

Architectural Patterns

By: Anupama Murali, Harihara Subramanian 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)

Big data platform

Any software or hardware platform should support large datasets; otherwise, it is hard to support those large datasets with traditional database tools:

The preceding diagram depicts a sample big data platform with supported sample tools, servers, hardware, and so on.

Big data engineering

Big data engineering gets the most value out of the vast amount of disparate data, data staging, profiling, and data cleansing in any big data platform. Also, it represents optimal ways of migrating the data from back office systems to the front office to help data analysts and data scientists:

The preceding diagram accounts for a sample ecosystem of a big data engineering landscape. One can find numerous tools in each...