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)

Theory

Clustering is the machine learning task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Given a set of data points, we can use a clustering algorithm to group each data point into a specific group. In theory, data points that are clustered in the same group should have similar properties or features, while data points in different groups should have highly distinct properties or features. Clustering is a common technique for statistical data analysis, and is used in many fields.

There are different types of clustering algorithm. The following are the most common clustering algorithms:

  • K-means clustering algorithm
  • Mean-shift clustering
  • Agglomerative-hierarchical clustering
  • Density-Based Spatial Clustering

We use clustering for IMDb because similar datasets are very close to each other...