Book Image

Hands-On Big Data Modeling

By : James Lee, Tao Wei, Suresh Kumar Mukhiya
Book Image

Hands-On Big Data Modeling

By: James Lee, Tao Wei, Suresh Kumar Mukhiya

Overview of this book

Modeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements. To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets. By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently.
Table of Contents (17 chapters)

Non-DBMS-based approach to big data

In this chapter, we will focus on central issues in large-scale data processing and data management, and when we should use a DBMS that can perform parallel operations versus when should we use a Hadoop or Yarn-style system. In this section, we are going to address two contrasting approaches to handling a high volume of data, filesystems, and DBMS.

Filesystems

The filesystem-oriented approach is the traditional method used in the early days of data processing. However, several applications dealing with simple and small datasets use this approach even today. Data is stored and processed using separate files in this approach. For example, each user defines and implements their own files that...