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 data analytics

Data analytics is the process of applying qualitative and quantitative techniques when examining data with the goal of providing valuable insights. Using various techniques and concepts, data analytics can provide the means to explore the data Exploratory Data Analysis (EDA) as well as draw conclusions about the data Confirmatory Data Analysis (CDA). EDA and CDA are fundamental concepts of data analytics, and it is important to understand the difference between the two.

EDA involves methodologies, tools, and techniques used to explore data with the intention of finding patterns in the data and relationships between various elements of the data. CDA involves methodologies, tools, and techniques used to provide an insight or conclusion on a specific question based on a hypothesis and statistical techniques or simple observation of the data.

A quick...