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

The data lake, data warehouse, and data mart

To build a data architecture, an architect needs to understand the basic concept and differences between a data lake, data warehouse, and data mart. In this section, we will cover the modern data architectural ecosystem and where the data lake, data warehouse, and data mart fit into that landscape.

The following diagram depicts the landscape of a modern data architecture:

Figure 2.5 – Landscape of a modern data architecture

As we can see, various types of data get ingested into the data lake, where it lands in the raw zone. The data lake consists of structured, semi-structured, and unstructured data ingested directly from data sources. Data lakes have a zone consisting of cleansed, transformed, and sorted datasets that serve various downstream data processing activities such as data analytics, advanced analytics, publishing as Data-as-a-Service, AI, ML, and many more. This is called the curated zone. The...