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

Understanding data types, formats, and encodings

In this section, you will learn about the various data types and data formats. We will also cover compression and how compression and formats go together. After that, we will briefly discuss data encodings. This section will prepare you to understand these basic features of data, which will be of use when we discuss data storage and databases in the upcoming sections.

Data types

All datasets that are used in modern-day data engineering can be broadly classified into one of three categories, as follows:

  • Structured data: This is a type of dataset that can easily be mapped to a predefined structure or schema. It usually refers to the relational data model, where each data element can be mapped to a predefined field. In a structured dataset, usually, the number of fields, their data type, and the order of the fields are well defined. The most common example of this is a relational data structure where we model the data structure...