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

Overview of streaming

Stream processing is the act of continuously computing results as new data becomes available. A very simple example of this is computing the average of some numbers in a continuous fashion. To begin with, we start with the following information:

  • Number of items = 0
  • Current average = 0

As a new number comes in, we perform the following steps:

  1. Compute a new total = Number of items x Current average + New number
  2. Increment the number of items by one
  3. Set the current average = New total / Number of items

As you can see, the continuous average computation algorithm is quite different from the batch-oriented algorithm. It is important to bear in mind the following facts when using this algorithm:

  • The average value gets updated as new numbers become available
  • The previously computed average value is reused to compute a new average

The following recipe using...