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)

Spark Streaming

Spark Streaming is not the first streaming architecture to come into existence. Several technologies have existenced over time to deal with the real-time processing needs of various business use cases. Twitter Storm was one of the first popular stream processing technologies out there and was in used by many organizations fulfilling the needs of many businesses.

Apache Spark comes with a streaming library, which has rapidly evolved to be the most widely used technology. Spark Streaming has some distinct advantages over the other technologies, the first and foremost being the tight integration between Spark Streaming APIs and the Spark core APIs making building a dual purpose real-time and batch analytical platform feasible and efficient than otherwise. Spark Streaming also integrates with Spark ML and Spark SQL, as well as GraphX, making it the most powerful stream...