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

Summary

In this chapter, we covered the various data types and data formats that are available. We also discussed the various popular data formats that are used in modern data engineering and the compression techniques that are compatible with each. Once we understood the data types and formats, we explored various data storage formats – file, block, and object storage – we can use to store the data. Then, we discussed various kinds of enterprise data repositories in detail – data lake, data warehouse, and data marts. Once we covered the basics of data, including the different types and their storage, we briefly discussed databases and their types. We discussed various examples of databases, the USP of each kind of database, and when a particular kind of database should be chosen over another. We explored possible use cases when a database should be used.

Finally, we briefly covered the basic design considerations that a data architect should keep in mind while...