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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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Broadcast variables

Broadcast variables are shared variables across all executors. Broadcast variables are created once in the Driver and then are read only on executors. While it is simple to understand simple datatypes broadcasted, such as an Integer, broadcast is much bigger than simple variables conceptually. Entire datasets can be broadcasted in a Spark cluster so that executors have access to the broadcasted data. All the tasks running within an executor all have access to the broadcast variables.

Broadcast uses various optimized methods to make the broadcasted data accessible to all executors. This is an important challenge to solve as if the size of the datasets broadcasted is significant, you cannot expect 100s or 1000s of executors to connect to the Driver and pull the dataset. Rather, the executors pull the data via HTTP connection and the more recent addition which...

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