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Build efficient data flow and machine learning programs with this
flexible, multi-functional open-source cluster-computing framework
Key Features Master the art of real-time big data processing and
machine learning Explore a wide range of use-cases to analyze large
data Discover ways to optimize your work by using many features of
Spark 2.x and Scala Book DescriptionApache Spark is an in-memory,
cluster-based data processing system that provides a wide range of
functionalities such as big data processing, analytics, machine
learning, and more. With this Learning Path, you can take your
knowledge of Apache Spark to the next level by learning how to
expand Spark's functionality and building your own data flow and
machine learning programs on this platform. You will work with the
different modules in Apache Spark, such as interactive querying
with Spark SQL, using DataFrames and datasets, implementing
streaming analytics with Spark Streaming, and applying machine
learning and deep learning techniques on Spark using MLlib and
various external tools. By the end of this elaborately designed
Learning Path, you will have all the knowledge you need to master
Apache Spark, and build your own big data processing and analytics
pipeline quickly and without any hassle. This Learning Path
includes content from the following Packt products: Mastering
Apache Spark 2.x by Romeo Kienzler Scala and Spark for Big Data
Analytics by Md. Rezaul Karim, Sridhar Alla Apache Spark 2.x
Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi
Rajendran, Broderick Hall, Shuen MeiCookbook What you will learn
Get to grips with all the features of Apache Spark 2.x Perform
highly optimized real-time big data processing Use ML and DL
techniques with Spark MLlib and third-party tools Analyze
structured and unstructured data using SparkSQL and GraphX
Understand tuning, debugging, and monitoring of big data
applications Build scalable and fault-tolerant streaming
applications Develop scalable recommendation engines Who this book
is forIf you are an intermediate-level Spark developer looking to
master the advanced capabilities and use-cases of Apache Spark 2.x,
this Learning Path is ideal for you. Big data professionals who
want to learn how to integrate and use the features of Apache Spark
and build a strong big data pipeline will also find this Learning
Path useful. To grasp the concepts explained in this Learning Path,
you must know the fundamentals of Apache Spark and Scala.
Advanced analytics on your Big Data with latest Apache Spark 2.x
About This Book * An advanced guide with a combination of
instructions and practical examples to extend the most up-to date
Spark functionalities. * Extend your data processing capabilities
to process huge chunk of data in minimum time using advanced
concepts in Spark. * Master the art of real-time processing with
the help of Apache Spark 2.x Who This Book Is For If you are a
developer with some experience with Spark and want to strengthen
your knowledge of how to get around in the world of Spark, then
this book is ideal for you. Basic knowledge of Linux, Hadoop and
Spark is assumed. Reasonable knowledge of Scala is expected. What
You Will Learn * Examine Advanced Machine Learning and DeepLearning
with MLlib, SparkML, SystemML, H2O and DeepLearning4J * Study
highly optimised unified batch and real-time data processing using
SparkSQL and Structured Streaming * Evaluate large-scale Graph
Processing and Analysis using GraphX and GraphFrames * Apply Apache
Spark in Elastic deployments using Jupyter and Zeppelin Notebooks,
Docker, Kubernetes and the IBM Cloud * Understand internal details
of cost based optimizers used in Catalyst, SystemML and GraphFrames
* Learn how specific parameter settings affect overall performance
of an Apache Spark cluster * Leverage Scala, R and python for your
data science projects In Detail Apache Spark is an in-memory
cluster-based parallel processing system that provides a wide range
of functionalities such as graph processing, machine learning,
stream processing, and SQL. This book aims to take your knowledge
of Spark to the next level by teaching you how to expand Spark's
functionality and implement your data flows and machine/deep
learning programs on top of the platform. The book commences with
an overview of the Spark ecosystem. It will introduce you to
Project Tungsten and Catalyst, two of the major advancements of
Apache Spark 2.x. You will understand how memory management and
binary processing, cache-aware computation, and code generation are
used to speed things up dramatically. The book extends to show how
to incorporate H20, SystemML, and Deeplearning4j for machine
learning, and Jupyter Notebooks and Kubernetes/Docker for
cloud-based Spark. During the course of the book, you will learn
about the latest enhancements to Apache Spark 2.x, such as
interactive querying of live data and unifying DataFrames and
Datasets. You will also learn about the updates on the APIs and how
DataFrames and Datasets affect SQL, machine learning, graph
processing, and streaming. You will learn to use Spark as a big
data operating system, understand how to implement advanced
analytics on the new APIs, and explore how easy it is to use Spark
in day-to-day tasks. Style and approach This book is an extensive
guide to Apache Spark modules and tools and shows how Spark's
functionality can be extended for real-time processing and storage
with worked examples.
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