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 RDDs

A Resilient Distributed Dataset (RDD) is an immutable, distributed collection of objects. Spark RDDs are resilient or fault tolerant, which enables Spark to recover the RDD in the face of failures. Immutability makes the RDDs read-only once created. Transformations allow operations on the RDD to create a new RDD but the original RDD is never modified once created. This makes RDDs immune to race conditions and other synchronization problems.

The distributed nature of the RDDs works because an RDD only contains a reference to the data, whereas the actual data is contained within partitions across the nodes in the cluster.

Conceptually, a RDD is a distributed collection of elements spread out across multiple nodes in the cluster. We can simplify a RDD to better understand by thinking of a RDD as a large array of integers distributed across machines.

A RDD is...