Scalable Data Architecture with Java: Build efficient enterprise-grade data architecting solutions using Java

Orchestrate data architecting solutions using Java and related technologies to evaluate, recommend and present the most suitable solution to leadership and clients

Key Features

• Learn how to adapt to the ever-evolving data architecture technology landscape
• Understand how to choose the best suited technology, platform, and architecture to realize effective business value
• Implement effective data security and governance principles

Book Description

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.

What you will learn

• Analyze and use the best data architecture patterns for problems
• Understand when and how to choose Java tools for a data architecture
• Build batch and real-time data engineering solutions using Java
• Discover how to apply security and governance to a solution
• Measure performance, publish benchmarks, and optimize solutions
• Evaluate, choose, and present the best architectural alternatives
• Understand how to publish Data as a Service using GraphQL and a REST API

Who this book is for

Data architects, aspiring data architects, Java developers and anyone who wants to develop or optimize scalable data architecture solutions using Java will find this book useful. A basic understanding of data architecture and Java programming is required to get the best from this book.

Table of Contents

1. Basics of Modern Data Architecture
2. Data Storage and Databases
3. Identifying the Right Data Platform
4. ETL Data Load - A Batch-Based Solution to Ingest Data in a Data Warehouse
5. Architecting a Batch Processing Pipeline
6. Architecting a Real-Time Processing Pipeline
7. Core Architectural Design Patterns
8. Enabling Data Security and Governance
9. Exposing MongoDB Data as a Service
10. Federated and Scalable DaaS with GraphQL
11. Measuring Performance and Benchmarking Your Applications
12. Evaluating, Recommending, and Presenting Your Solutions

Sinchan Banerjee is a Principal Data architect who has vast experience in architecting, building and leading multiple data engineering solutions in healthcare and finance domain. His expertise lies in architecting, designing, developing, and delivering high volume fast paced data engineering solutions using cutting-edge Big Data and Streaming technologies over cloud platforms like AWS, Azure and OpenShift, and on-premises. He has rich experience in designing, building and deploying microservices using SOAP, REST and GraphQL over Kubernetes and Docker in the cloud platforms as well as in on-premises. He has also considerable experience in creating proposal, present and enables to win multimillion dollar business deals for his employer. He is currently a Principal Data architect at UST Inc working for their client ElevanceHealth (Formerly Anthem) and leading Anthem’s digitalization journey into cloud. Prior to this, he has worked for multiple organizations as a Data architect at American Express, Data-core Systems Inc., UnitedHealth Group and as data engineer at Impetus and Hewlett-Packard. He has a bachelor’s degree in Computer Science and Engineering from SRM University and is certified in Machine learning with Big Data Analytics from University of California. He is also a certified AWS professional and a certified Java programmer. He is the lead inventor of a patent (on Storage Capacity Forecasting using Predictive Analysis and has co-authored multiple international publications. He is the recipient of multiple awards and accolades throughout his career from multiple organizations like Hewlett-Packard, UnitedHealth Group and UST, for his technical contribution, innovation, and leadership skills. He currently resides in the United States along with his family and pets.