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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Big Data and Data Management
Introduction to Big Data and Data Management
The concept of big data
Sources and types of big data
Introduction to big data modeling
Introduction to managing big data
Importance and implications of big data modeling and management
Setting up big data modeling platforms
Summary
Further reading
2
Data Modeling and Management Platforms
Data Modeling and Management Platforms
Big data management
Big data management services
Big data management vendors
Big data storage and data models
Big data programming models
Getting started with Python and R
Summary
Further reading
3
Defining Data Models
Defining Data Models
Data model structures
Data operations
Data constraints
A unified approach to big data modeling and data management
Summary
Further reading
4
Categorizing Data Models
Categorizing Data Models
Levels of data modeling
Types of data model
Summary
Further reading
5
Structures of Data Models
Structures of Data Models
Semi-structured data models
VSM with Lucene
Graph-data models
Summary
Further reading
6
Modeling Structured Data
Modeling Structured Data
Getting started with structured data
Modeling structured data using Python
Summary
Further reading
7
Modeling with Unstructured Data
Modeling with Unstructured Data
Getting started with unstructured data
Tools for analyzing unstructured data
Summary
Further reading
8
Modeling with Streaming Data
Modeling with Streaming Data
Data stream and data model versus data format
Why is streaming data different?
Importance and implications of streaming data
Exploring streaming sensor data from the Twitter API
Summary
Further reading
9
Streaming Sensor Data
Streaming Sensor Data
Sensor data
Data lakes
Exploring streaming sensor data from a weather station
Summary
Further study
10
Concept and Approaches of Big-Data Management
Concept and Approaches of Big-Data Management
Non-DBMS-based approach to big data
DBMS-based approach to big data
Parallel and distributed DBMS
DBMS and MapReduce-style systems
Summary
Further reading
11
DBMS to BDMS
DBMS to BDMS
Characteristics of BDMS
Exploring data management with Redis
Aerospike
AsterixDB
Summary
Further reading
12
Modeling Bitcoin Data Points with Python
Modeling Bitcoin Data Points with Python
Introduction to Bitcoin data
Theory
Importing Bitcoin data into iPython
Preprocessing and model creation
Predicting Bitcoin price using Recurrent Neural Network
Summary
Further reading
13
Modeling Twitter Feeds Using Python
Modeling Twitter Feeds Using Python
Importing Twitter feed data
Modeling Twitter feeds
Summary
Further reading
14
Modeling Weather Data Points with Python
Modeling Weather Data Points with Python
Introduction to weather data
Importing data
Modeling with data
Summary
Further reading
15
Modeling IMDb Data Points with Python
Modeling IMDb Data Points with Python
Introduction to IMDb data
Theory
Modeling with the IMDb dataset
Summary
Further reading
16
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Getting started with unstructured data

Unstructured data analysis has become a buzz phrase recently. A lot of researchers, data analysts, data modelers, and data scientists are struggling to find novel methods to enhance precision in unstructured data analysis. There are different sources of unstructured data. The most common sources include the following:

  • Email correspondence, and the reply, response, forward threads
  • Social media posts and activities
  • Video, sound, or image files
  • Web-browsing behavior

According to IBM, 90% of the world's data was produced in the past 2 years, which is unstructured or semi-structured. Due to its very nature, unstructured data is tremendously difficult to analyze. In the past, analysts were not able to do much about it due to the lack of technology. But with the upsurge of ICT technology, big data analysis, data mining, and machine learning...

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