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

A practical use case – exposing federated data models using GraphQL

In this section, we will learn how to develop DaaS using GraphQL in Java. To implement the solution, we will publish the same set of APIs that we published earlier using REST, but this time, we will implement the solution using GraphQL.

Before we start implementing GraphQL, it is important to design the GraphQL schema for our use case. In our use case, we need to read credit card applications from MongoDB using either the application ID or consumer ID. This was why we needed two separate endpoints in the REST-based solution (please refer to Chapter 9, Exposing MongoDB Data as a Service, for the REST-based DaaS solution).

Let’s analyze the requirements from a different perspective – that is, while considering the GraphQL-based solution. The biggest difference that GraphQL makes is that it reduces the number of endpoints, as well as the number of calls. So, for our use case, we will have a...