Book Image

Scalable Data Architecture with Java

By : Sinchan Banerjee
Book Image

Scalable Data Architecture with Java

By: Sinchan Banerjee

Overview of this book

Java architectural patterns and tools help architects to build reliable, scalable, and secure data engineering solutions that collect, manipulate, and publish data. This book will help you make the most of the architecting data solutions available with clear and actionable advice from an expert. You’ll start with an overview of data architecture, exploring responsibilities of a Java data architect, and learning about various data formats, data storage, databases, and data application platforms as well as how to choose them. Next, you’ll understand how to architect a batch and real-time data processing pipeline. You’ll also get to grips with the various Java data processing patterns, before progressing to data security and governance. The later chapters will show you how to publish Data as a Service and how you can architect it. Finally, you’ll focus on how to evaluate and recommend an architecture by developing performance benchmarks, estimations, and various decision metrics. By the end of this book, you’ll be able to successfully orchestrate data architecture solutions using Java and related technologies as well as to evaluate and present the most suitable solution to your clients.
Table of Contents (19 chapters)
1
Section 1 – Foundation of Data Systems
5
Section 2 – Building Data Processing Pipelines
11
Section 3 – Enabling Data as a Service
14
Section 4 – Choosing Suitable Data Architecture

Summary

In this chapter, we discussed various virtualization platforms. First, we briefly covered the architectures of the virtualization, containerization, and container orchestration frameworks. Then, we deployed VMs, Docker containers, and Kubernetes containers and ran an application on top of them. In doing so, we learned how to configure Dockerfiles and Kubernetes deployment scripts. After that, we discussed the Hadoop architecture and the various Hadoop distributions that are available on the market. Then, we briefly discussed cloud computing and its basic concepts. Finally, we covered the decisions that every data architect has to make: containers or VMs? Do I need big data processing? Cloud or on-premise? If the cloud, which cloud?

With that, we have a good understanding of some of the basic concepts and nuances of data architecting, including the basic concepts, databases, data storage, and the various platforms these solutions run on in production. In the next chapter...