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)

Introduction to machine learning

In this section, we will try to define machine learning from computer science, statistics, and data analytical perspectives. Machine learning (ML) is the branch of computer science that provides the computers the ability to learn without being explicitly programmed (Arthur Samuel in 1959). This field of study being evolved from the study of pattern recognition and computational learning theory in artificial intelligence.

More specifically, ML explores the study and construction of algorithms that can learn from heuristics and make predictions on data. This kind of algorithms overcome the strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Now let's more explicit and versatile definition from Prof. Tom M. Mitchell, who explained what machine learning really means...