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

Tools for performance engineering

In this section, we will briefly discuss various performance engineering tools.

The following are the different categories of performance engineering tools available:

  • Observability tools: These tools monitor and gather information about the application. These tools potentially help to identify bottlenecks, track throughput and latency, memory usage, and so on. In data engineering, each system is different, and the throughput and latency requirements are also different. Observability tools help identify if our application is lagging in terms of throughput or latency and by how much. They also help identify hidden issues that may only show up in the long run, in production. For example, a small memory leak in the application may not be noticeable within a few days of deployment. When such an application keeps on running, the tenured region of JVM heap space keeps slowly increasing until it overruns the heap space. The following are a few examples...