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)

GraphX

As shown in the preceding section, we can model many real-life use cases as Graphs with a set of vertices and a set of edges linking the vertices. We also wrote simple code trying to implement some basic graph operations and queries such as, Is X a friend of Y ? However, as we explored further, the algorithms only get more complicated along with use cases and also the size of graphs is much much larger than can be handled on one machine.

It is not possible to fit one billion Facebook users along with all their friendship relations into one machine or even a few machines.

What we need to do is to look beyond the one machine and few machines thrown together and rather start considering highly scalable architectures to implement the complex graph algorithms, which can handle the volume of data and complex interconnections of the data elements. We have already seen an introduction...