Book Image

Scala and Spark for Big Data Analytics

By : Md. Rezaul Karim, Sridhar Alla
Book Image

Scala and Spark for Big Data Analytics

By: Md. Rezaul Karim, Sridhar Alla

Overview of this book

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you. The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment. You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio. By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents (19 chapters)

The decision trees

In this section, we will discuss the DT algorithm in detail. A comparative analysis of Naive Bayes and DT will be discussed too. DTs are commonly considered as a supervised learning technique used for solving classification and regression tasks. A DT is simply a decision support tool that uses a tree-like graph (or a model of decisions) and their possible consequences, including chance event outcomes, resource costs, and utility. More technically, each branch in a DT represents a possible decision, occurrence, or reaction in terms of statistical probability.

Compared to Naive Bayes, DT is a far more robust classification technique. The reason is that at first DT splits the features into training and test set. Then it produces a good generalization to infer the predicted labels or classes. Most interestingly, DT algorithm can handle both binary and multiclass...