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Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

By : Karim, Sridhar Alla
2.8 (12)
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Scala and Spark for Big Data Analytics

Scala and Spark for Big Data Analytics

2.8 (12)
By: 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)
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Unsupervised learning

In this section, we will provide a brief introduction to unsupervised machine learning technique with appropriate examples. Let's start the discussion with a practical example. Suppose you have a large collection of not-pirated-totally-legal mp3s in a crowded and massive folder on your hard drive. Now, what if you can build a predictive model that helps automatically group together similar songs and organize them into your favorite categories such as country, rap, rock, and so on. This act of assigning an item to a group such that a mp3 to is added to the respective playlist in an unsupervised way. In the previous chapters, we assumed you're given a training dataset of correctly labeled data. Unfortunately, we don't always have that extravagance when we collect data in the real-world. For example, suppose we would like to divide up a large...

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