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

Architecting the solution

To architect the solution, let’s summarize the analysis we discussed in the previous section. Here are the conclusions we can make:

  • This is a real-time data engineering problem
  • This problem can be solved using a streaming platform such as Kafka or Kinesis
  • 1 million events will be published daily, with a chance of the volume of events increasing over time
  • The solution should be hosted on a hybrid platform, where data processing and analysis are done on-premise and the results are stored in the cloud for easy retrieval

Since our streaming platform is on-premise and can be maintained on on-premise servers, Apache Kafka is a great choice. It supports a distributed, fault-tolerant, robust, and reliable architecture. It can be easily scaled by increasing the number of partitions and provides an at-least-once delivery guarantee (which ensures that at least one copy of all events will be delivered without event drops).

Now, let...