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Scala and Spark for Big Data Analytics
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Scala and Spark for Big Data Analytics
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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.
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Current Title:
Scala and Spark for Big Data Analytics
Table of Contents (19 chapters)
Preface
Free Chapter
Introduction to Scala
Object-Oriented Scala
Functional Programming Concepts
Collection APIs
Tackle Big Data – Spark Comes to the Party
Start Working with Spark – REPL and RDDs
Special RDD Operations
Introduce a Little Structure - Spark SQL
Stream Me Up, Scotty - Spark Streaming
Everything is Connected - GraphX
Learning Machine Learning - Spark MLlib and Spark ML
My Name is Bayes, Naive Bayes
Time to Put Some Order - Cluster Your Data with Spark MLlib
Text Analytics Using Spark ML
Spark Tuning
Time to Go to ClusterLand - Deploying Spark on a Cluster
Testing and Debugging Spark
Customer Reviews