A handy reference guide for data analysts and data scientists to
help to obtain value from big data analytics using Spark on Hadoop
clusters About This Book * This book is based on the latest 2.0
version of Apache Spark and 2.7 version of Hadoop integrated with
most commonly used tools. * Learn all Spark stack components
including latest topics such as DataFrames, DataSets, GraphFrames,
Structured Streaming, DataFrame based ML Pipelines and SparkR. *
Integrations with frameworks such as HDFS, YARN and tools such as
Jupyter, Zeppelin, NiFi, Mahout, HBase Spark Connector,
GraphFrames, H2O and Hivemall. Who This Book Is For Though this
book is primarily aimed at data analysts and data scientists, it
will also help architects, programmers, and practitioners.
Knowledge of either Spark or Hadoop would be beneficial. It is
assumed that you have basic programming background in Scala,
Python, SQL, or R programming with basic Linux experience. Working
experience within big data environments is not mandatory. What You
Will Learn * Find out and implement the tools and techniques of big
data analytics using Spark on Hadoop clusters with wide variety of
tools used with Spark and Hadoop * Understand all the Hadoop and
Spark ecosystem components * Get to know all the Spark components:
Spark Core, Spark SQL, DataFrames, DataSets, Conventional and
Structured Streaming, MLLib, ML Pipelines and Graphx * See batch
and real-time data analytics using Spark Core, Spark SQL, and
Conventional and Structured Streaming * Get to grips with data
science and machine learning using MLLib, ML Pipelines, H2O,
Hivemall, Graphx, SparkR and Hivemall. In Detail Big Data Analytics
book aims at providing the fundamentals of Apache Spark and Hadoop.
All Spark components - Spark Core, Spark SQL, DataFrames, Data
sets, Conventional Streaming, Structured Streaming, MLlib, Graphx
and Hadoop core components - HDFS, MapReduce and Yarn are explored
in greater depth with implementation examples on Spark + Hadoop
clusters. It is moving away from MapReduce to Spark. So, advantages
of Spark over MapReduce are explained at great depth to reap
benefits of in-memory speeds. DataFrames API, Data Sources API and
new Data set API are explained for building Big Data analytical
applications. Real-time data analytics using Spark Streaming with
Apache Kafka and HBase is covered to help building streaming
applications. New Structured streaming concept is explained with an
IOT (Internet of Things) use case. Machine learning techniques are
covered using MLLib, ML Pipelines and SparkR and Graph Analytics
are covered with GraphX and GraphFrames components of Spark.
Readers will also get an opportunity to get started with web based
notebooks such as Jupyter, Apache Zeppelin and data flow tool
Apache NiFi to analyze and visualize data. Style and approach This
step-by-step pragmatic guide will make life easy no matter what
your level of experience. You will deep dive into Apache Spark on
Hadoop clusters through ample exciting real-life examples.
Practical tutorial explains data science in simple terms to help
programmers and data analysts get started with Data Science
General
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