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

Core Architectural Design Patterns

In the previous chapters, we learned how to architect data engineering solutions for both batch-based and real-time processing using specific use cases. However, we haven’t discussed the various options available concerning architectural design patterns for batch and real-time stream processing engines.

In this chapter, we will learn about a few commonly used architectural patterns for data engineering problems. We will start by learning about a few common patterns in batch-based data processing and common scenarios where they are used. Then, we will learn about various streaming-based processing patterns in modern data architectures and how they can help solve business problems. We will also discuss the two famous hybrid data architectural patterns. Finally, we will learn about various serverless data ingestion patterns commonly used in the cloud.

In this chapter, we will cover the following topics:

  • Core batch processing patterns...