Hands-On Big Data Modeling
Effective database design techniques for data architects and business intelligence professionals
Published on 30. November 2018
Book
Paperback/Softback
306 pages
978-1-78862-090-1 (ISBN)
Description
Solve all big data problems by learning how to create efficient data models
Key Features
Create effective models that get the most out of big data
Apply your knowledge to datasets from Twitter and weather data to learn big data
Tackle different data modeling challenges with expert techniques presented in this book
Book DescriptionModeling 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.
What you will learn
Get insights into big data and discover various data models
Explore conceptual, logical, and big data models
Understand how to model data containing different file types
Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling
Create data models such as Graph Data and Vector Space
Model structured and unstructured data using Python and R
Who this book is forThis book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.
Key Features
Create effective models that get the most out of big data
Apply your knowledge to datasets from Twitter and weather data to learn big data
Tackle different data modeling challenges with expert techniques presented in this book
Book DescriptionModeling 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.
What you will learn
Get insights into big data and discover various data models
Explore conceptual, logical, and big data models
Understand how to model data containing different file types
Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling
Create data models such as Graph Data and Vector Space
Model structured and unstructured data using Python and R
Who this book is forThis book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 17 mm
Weight
575 gr
ISBN-13
978-1-78862-090-1 (9781788620901)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
James Lee | Tao Wei | Suresh Kumar Mukhiya
Hands-On Big Data Modeling
Effective database design techniques for data architects and business intelligence professionals
E-Book
09/2024
1st Edition
Packt Publishing Limited
€31.49
Available for download
Persons
James Lee is a passionate software wizard working at one of the top Silicon Valley-based start-ups specializing in big data analysis. In the past, he has worked at big companies such as Google and Amazon. In his day job, he works with big data technologies, including Cassandra and Elasticsearch, and is an absolute Docker technology geek and IntelliJ IDEA lover with a strong focus on efficiency and simplicity. Apart from his career as a software engineer, he is keen on sharing his knowledge with others and guiding them, especially in relation to start-ups and programming. He has been teaching courses and conducting workshops on Java programming / IntelliJ IDEA since he was 21. James holds an MS degree in computer science from McGill University and has many years' experience as a teaching assistant in a variety of computer science classes. He also enjoys skiing and swimming, and is a passionate traveler. Tao Wei is a passionate software engineer who works in a leading Silicon Valley-based big data analysis company. Previously, Tao worked in big IT companies, such as IBM and Cisco. He has intensive experience in designing and building distributed, large-scale systems with proven high availability and reliability. Tao has an MS degree in computer science from McGill University and many years of experience as a teaching assistant in various computer science classes. When not working, he enjoys reading and swimming, and is a passionate photographer. Suresh Kumar Mukhiya is a PhD candidate currently associated with Western Norway University of Applied Sciences (HVL). He is also a web application developer and big data enthusiast specializing in information systems, model-driven software engineering, big data analysis, and artificial intelligence. He has completed a masters in information systems from the Norwegian University of Science and Technology, along with a thesis in processing mining. He also holds a bachelor's degree in computer science and information technology (BSc.CSIT).
Content
Table of Contents
Introduction to Big Data and Data Management
Data Modeling and Data Management platforms for Big Data
Defining Data Model
Categorizing Data Model
Structures of Data Model
Modeling Structured Data
Modeling with Unstructured Data
Modeling with Steaming Data
Streaming Sensors Data
Concept and Approaches of Big Data Management
DBMS to BDMS
Big Data Management Services and Vendors
Modeling Twitter Feeds using Python
Modeling Weather Data Points with Python
Modeling IMDB Data Points with Python
Introduction to Big Data and Data Management
Data Modeling and Data Management platforms for Big Data
Defining Data Model
Categorizing Data Model
Structures of Data Model
Modeling Structured Data
Modeling with Unstructured Data
Modeling with Steaming Data
Streaming Sensors Data
Concept and Approaches of Big Data Management
DBMS to BDMS
Big Data Management Services and Vendors
Modeling Twitter Feeds using Python
Modeling Weather Data Points with Python
Modeling IMDB Data Points with Python