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

ETL Data Load – A Batch-Based Solution to Ingesting Data in a Data Warehouse

In the previous chapters, we discussed various foundational concepts surrounding data engineering, starting with the different types of data engineering problems. Then, we discussed various data types, data formats, data storage, and databases. We also discussed the various platforms that are available to deploy and run data engineering solutions in production.

In this chapter, we will learn how to architect and design a batch-based solution for low to medium-volume data ingestion from a data source to a data warehouse. Here, we will be taking a real-time use case to discuss, model, and design a data warehouse for such a scenario. We will also learn how to develop this solution using a Java-based technical stack and run and test our solution. By the end of this chapter, you should be able to design and develop an extract, transform, load (ETL)-based batch pipeline using Java and its related stack...