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)

Joins

In traditional databases, joins are used to join one transaction table with another lookup table to generate a more complete view. For example, if you have a table of online transactions by customer ID and another table containing the customer city and customer ID, you can use join to generate reports on the transactions by city.

Transactions table: The following table has three columns, the CustomerID, the Purchased item, and how much the customer paid for the item:

CustomerID Purchased item Price paid
1 Headphone 25.00
2 Watch 100.00
3 Keyboard 20.00
1 Mouse 10.00
4 Cable 10.00
3 Headphone 30.00

Customer Info table: The following table has two columns, the CustomerID and the City the customer lives in:

CustomerID City
1 Boston
2 New York
3 Philadelphia
4 Boston

Joining the transaction table with the customer info table will generate...