Harness the power of Scala to program Spark and analyze tonnes of
data in the blink of an eye! About This Book * Learn Scala's
sophisticated type system that combines Functional Programming and
object-oriented concepts * Work on a wide array of applications,
from simple batch jobs to stream processing and machine learning *
Explore the most common as well as some complex use-cases to
perform large-scale data analysis with Spark Who This Book Is For
Anyone who wishes to learn how to perform data analysis by
harnessing the power of Spark will find this book extremely useful.
No knowledge of Spark or Scala is assumed, although prior
programming experience (especially with other JVM languages) will
be useful to pick up concepts quicker. What You Will Learn *
Understand object-oriented & functional programming concepts of
Scala * In-depth understanding of Scala collection APIs * Work with
RDD and DataFrame to learn Spark's core abstractions * Analysing
structured and unstructured data using SparkSQL and GraphX *
Scalable and fault-tolerant streaming application development using
Spark structured streaming * Learn machine-learning best practices
for classification, regression, dimensionality reduction, and
recommendation system to build predictive models with widely used
algorithms in Spark MLlib & ML * Build clustering models to
cluster a vast amount of data * Understand tuning, debugging, and
monitoring Spark applications * Deploy Spark applications on real
clusters in Standalone, Mesos, and YARN In Detail 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. Style
and approach Filled with practical examples and use cases, this
book will hot only help you get up and running with Spark, but will
also take you farther down the road to becoming a data scientist.
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