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)

Learning Machine Learning - Spark MLlib and Spark ML

"Each of us, actually every animal, is a data scientist. We collect data from our sensors, and then we process the data to get abstract rules to perceive our environment and control our actions in that environment to minimize pain and/or maximize pleasure. We have memory to store those rules in our brains, and then we recall and use them when needed. Learning is lifelong; we forget rules when they no longer apply or revise them when the environment changes."

- Ethem Alpaydin, Machine Learning: The New AI

The purpose of this chapter is to provide a conceptual introduction to statistical machine learning (ML) techniques for those who might not normally be exposed to such approaches during their typical required statistical training. This chapter also aims to take a newcomer from having minimal knowledge of machine learning...