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Books > Computing & IT > Applications of computing > Databases > Data mining
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Learning Apache Spark 2 (Paperback)
Loot Price: R1,226
Discovery Miles 12 260
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Learning Apache Spark 2 (Paperback)
Expected to ship within 10 - 15 working days
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Learn about the fastest-growing open source project in the world,
and find out how it revolutionizes big data analytics About This
Book * Exclusive guide that covers how to get up and running with
fast data processing using Apache Spark * Explore and exploit
various possibilities with Apache Spark using real-world use cases
in this book * Want to perform efficient data processing at real
time? This book will be your one-stop solution. Who This Book Is
For This guide appeals to big data engineers, analysts, architects,
software engineers, even technical managers who need to perform
efficient data processing on Hadoop at real time. Basic familiarity
with Java or Scala will be helpful. The assumption is that readers
will be from a mixed background, but would be typically people with
background in engineering/data science with no prior Spark
experience and want to understand how Spark can help them on their
analytics journey. What You Will Learn * Get an overview of big
data analytics and its importance for organizations and data
professionals * Delve into Spark to see how it is different from
existing processing platforms * Understand the intricacies of
various file formats, and how to process them with Apache Spark. *
Realize how to deploy Spark with YARN, MESOS or a Stand-alone
cluster manager. * Learn the concepts of Spark SQL, SchemaRDD,
Caching and working with Hive and Parquet file formats * Understand
the architecture of Spark MLLib while discussing some of the
off-the-shelf algorithms that come with Spark. * Introduce yourself
to the deployment and usage of SparkR. * Walk through the
importance of Graph computation and the graph processing systems
available in the market * Check the real world example of Spark by
building a recommendation engine with Spark using ALS. * Use a
Telco data set, to predict customer churn using Random Forests. In
Detail Spark juggernaut keeps on rolling and getting more and more
momentum each day. Spark provides key capabilities in the form of
Spark SQL, Spark Streaming, Spark ML and Graph X all accessible via
Java, Scala, Python and R. Deploying the key capabilities is
crucial whether it is on a Standalone framework or as a part of
existing Hadoop installation and configuring with Yarn and Mesos.
The next part of the journey after installation is using key
components, APIs, Clustering, machine learning APIs, data
pipelines, parallel programming. It is important to understand why
each framework component is key, how widely it is being used, its
stability and pertinent use cases. Once we understand the
individual components, we will take a couple of real life advanced
analytics examples such as 'Building a Recommendation system',
'Predicting customer churn' and so on. The objective of these real
life examples is to give the reader confidence of using Spark for
real-world problems. Style and approach With the help of practical
examples and real-world use cases, this guide will take you from
scratch to building efficient data applications using Apache Spark.
You will learn all about this excellent data processing engine in a
step-by-step manner, taking one aspect of it at a time. This highly
practical guide will include how to work with data pipelines,
dataframes, clustering, SparkSQL, parallel programming, and such
insightful topics with the help of real-world use cases.
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