Book Image

Hands-On Data Analysis with Scala

By : Rajesh Gupta
Book Image

Hands-On Data Analysis with Scala

By: Rajesh Gupta

Overview of this book

Efficient business decisions with an accurate sense of business data helps in delivering better performance across products and services. This book helps you to leverage the popular Scala libraries and tools for performing core data analysis tasks with ease. The book begins with a quick overview of the building blocks of a standard data analysis process. You will learn to perform basic tasks like Extraction, Staging, Validation, Cleaning, and Shaping of datasets. You will later deep dive into the data exploration and visualization areas of the data analysis life cycle. You will make use of popular Scala libraries like Saddle, Breeze, Vegas, and PredictionIO for processing your datasets. You will learn statistical methods for deriving meaningful insights from data. You will also learn to create applications for Apache Spark 2.x on complex data analysis, in real-time. You will discover traditional machine learning techniques for doing data analysis. Furthermore, you will also be introduced to neural networks and deep learning from a data analysis standpoint. By the end of this book, you will be capable of handling large sets of structured and unstructured data, perform exploratory analysis, and building efficient Scala applications for discovering and delivering insights
Table of Contents (14 chapters)
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Section 1: Scala and Data Analysis Life Cycle
Section 2: Advanced Data Analysis and Machine Learning
Section 3: Real-Time Data Analysis and Scalability

Working with Data at Scale

Data is being produced at an accelerated pace with advancements in technology. The widespread usage and adoption of the Internet of Things (IoT) is a great example of this. These specifically purposed IoT devices are tens of billions in number and are growing rapidly. Many of these devices, using their sensors, continually produce observations as data. Even though the data might be small as a unit, combined together it becomes humongous. IoT is just one example of how much and how fast the data is being created.

This kind of data is sometimes referred to as big data that is too big to fit on a single machine for storage and computing purposes. Big data has three important properties:

  • Variety: Data in different formats and structures
  • Velocity: New data arriving at a fast rate
  • Volume: Huge overall data size

In the prior chapters, we learned how to deal...