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 batch processing patterns

In this section, we will look at a few commonly used data engineering patterns to solve batch processing problems. Although there can be many variations of the implementation, these patterns are generic, irrespective of the technologies used to implement the patterns. In the following sections, we’ll discuss the commonly used batch processing patterns.

The staged Collect-Process-Store pattern

The staged Collect-Process-Store pattern is the most common batch processing pattern. It is also commonly known as the Extract-Transform-Load (ETL) pattern in data engineering. This architectural pattern is used to ingest data and store it as information. The following diagram depicts this architectural pattern:

Figure 7.1 – The staged Collect-Process-Store pattern

We can break this pattern into a series of stages, as follows:

  1. In this architectural pattern, one or more data sources are extracted and kept in a form...