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)

My Name is Bayes, Naive Bayes

"Prediction is very difficult, especially if it's about the future"

-Niels Bohr

Machine learning (ML) in combination with big data is a radical combination that has created some great impacts in the field of research in Academia and Industry. Moreover, many research areas are also entering into big data since datasets are being generated and produced in an unprecedented way from diverse sources and technologies, commonly referred as the Data Deluge. This imposes great challenges on ML, data analytics tools, and algorithms to find the real VALUE out of big data criteria such as volume, velocity, and variety. However, making predictions from these huge dataset has never been easy.

Considering this challenge, in this chapter we will dig deeper into ML and find out how to use a simple yet powerful method to build a scalable classification...