Publisher's Synopsis
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 Description
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.
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 for
This 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.
