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 design patterns

This section covers most prominent big data design patterns by various data layers such as data sources and ingestion layer, data storage layer and data access layer.

Data sources and ingestion layer

Enterprise big data systems face a variety of data sources with non-relevant information (noise) alongside relevant (signal) data. Noise ratio is very high compared to signals, and so filtering the noise from the pertinent information, handling high volumes, and the velocity of data is significant. This is the responsibility of the ingestion layer. The common challenges in the ingestion layers are as follows:

  • Multiple data source load and prioritization
  • Ingested data indexing and tagging
  • Data validation...