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)

Summary

Throughout this chapter, you have learned the basics of the Scala programming language, its features, and available editor. We have also briefly discussed Scala and its syntax. We demonstrated the installation and setting up guidelines for beginners who are new to Scala programming. Later in the chapter, you learned how to write, compile, and execute a sample Scala code. Moreover, a comparative discussion about Scala and Java provided for those who are from a Java background. Here's a short comparison between Scala and Python:

Scala is statically typed, but Python is dynamically typed. Scala (mostly) embraces the functional programming paradigm, while Python doesn't. Python has a unique syntax that lacks most of the parentheses, while Scala (almost) always requires them. In Scala, almost everything is an expression; while this isn't true in Python. However, there are a few points on the upside that are seemingly convoluted. The type complexity is mostly optional. Secondly, according to the documentation provided by https://stackoverflow.com/questions/1065720/what-is-the-purpose-of-scala-programming-language/5828684#5828684, Scala compiler is like free testing and documentation as cyclomatic complexity and lines of code escalate. When aptly implemented Scala can perform otherwise all but impossible operations behind consistent and coherent APIs.

In next the chapter, we will discuss how to improve our experience on the basics to know how Scala implements the object oriented paradigm to allow building modular software systems.