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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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Optimization techniques

There are several aspects of tuning Spark applications toward better optimization techniques. In this section, we will discuss how we can further optimize our Spark applications by applying data serialization by tuning the main memory with better memory management. We can also optimize performance by tuning the data structure in your Scala code while developing Spark applications. The storage, on the other hand, can be maintained well by utilizing serialized RDD storage.

One of the most important aspects is garbage collection, and it's tuning if you have written your Spark application using Java or Scala. We will look at how we can also tune this for optimized performance. For distributed environment- and cluster-based system, a level of parallelism and data locality has to be ensured. Moreover, performance could further be improved by using broadcast...

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