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 learned how to analyze a data engineering requirement from scratch, draw a definite conclusion, and extract facts that will help us in our architectural decision-making process. Next, we learned how to profile source data and how such an analysis helps us build better data engineering solutions. Going further, we used facts, requirements, and our analysis to build a robust and effective architecture for a batch-based data engineering problem with a low or medium volume of data. Finally, we mapped the design to build an effective ETL batch-based data ingestion pipeline using Spring Batch and test it. Along the way, you learned how to analyze a data engineering problem from scratch and how to build similar pipelines effectively for when you are presented with a similar problem next time around.

Now that we have successfully architected and developed a batch-based solution for medium- and low-volume data engineering problems, in the next chapter, we will...