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LEADER 00000cam a2200625Ii 4500
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008 170317s2017 enkab o 001 0 eng d
019 1081417339
020 9781786466259|q(electronic bk.)
020 1786466252|q(electronic bk.)
029 1 GBVCP|b897169743
035 (OCoLC)976408019|z(OCoLC)1081417339
037 CL0500000840|bSafari Books Online
037 978A042E-251E-4460-88A6-41FFF582EF91|bOverDrive, Inc.
|nhttp://www.overdrive.com
040 UMI|beng|erda|epn|cUMI|dTEFOD|dOCLCF|dIDEBK|dSTF|dTOH
|dOCLCQ|dN$T|dCOO|dUOK|dCEF|dKSU|dDEBBG|dUAB|dYDX|dMOF|dAU
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049 INap
082 04 005.7
082 04 005.7|223
099 eBook O'Reilly for Public Libraries
100 1 Drabas, Tomasz,|eauthor.
245 10 Learning PySpark :|bbuild data-intensive applications
locally and deploy at scale using the combined powers of
Python and Spark 2.0 /|cTomasz Drabas, Denny Lee ;
foreword by Holden Karau.|h[O'Reilly electronic resource]
264 1 Birmingham, UK :|bPackt Publishing,|c2017.
300 1 online resource (1 volume) :|billustrations, maps
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
500 Includes index.
505 0 Cover -- Copyright -- Credits -- Foreword -- About the
Authors -- About the Reviewer -- www.PacktPub.com --
Customer Feedback -- Table of Contents -- Preface --
Chapter 1: Understanding Spark -- What is Apache Spark? --
Spark Jobs and APIs -- Execution process -- Resilient
Distributed Dataset -- DataFrames -- Datasets -- Catalyst
Optimizer -- Project Tungsten -- Spark 2.0 architecture --
Unifying Datasets and DataFrames -- Introducing
SparkSession -- Tungsten phase 2 -- Structured streaming -
- Continuous applications -- Summary -- Chapter 2:
Resilient Distributed Datasets -- Internal workings of an
RDD -- Creating RDDs -- Schema -- Reading from files --
Lambda expressions -- Global versus local scope --
Transformations -- The .map(...) transformation -- The
.filter(...) transformation -- The .flatMap(...)
transformation -- The .distinct(...) transformation -- The
.sample(...) transformation -- The .leftOuterJoin(...)
transformation -- The .repartition(...) transformation --
Actions -- The .take(...) method -- The .collect(...)
method -- The .reduce(...) method -- The .count(...)
method -- The .saveAsTextFile(...) method -- The
.foreach(...) method -- Summary -- Chapter 3: DataFrames -
- Python to RDD communications -- Catalyst Optimizer
refresh -- Speeding up PySpark with DataFrames -- Creating
DataFrames -- Generating our own JSON data -- Creating a
DataFrame -- Creating a temporary table -- Simple
DataFrame queries -- DataFrame API query -- SQL query --
Interoperating with RDDs -- Inferring the schema using
reflection -- Programmatically specifying the schema --
Querying with the DataFrame API -- Number of rows --
Running filter statements -- Querying with SQL -- Number
of rows -- Running filter statements using the where
Clauses -- DataFrame scenario -- on-time flight
performance -- Preparing the source datasets.
505 8 Joining flight performance and airports -- Visualizing our
flight-performance data -- Spark Dataset API -- Summary --
Chapter 4: Prepare Data for Modeling -- Checking for
duplicates, missing observations, and outliers --
Duplicates -- Missing observations -- Outliers -- Getting
familiar with your data -- Descriptive statistics --
Correlations -- Visualization -- Histograms --
Interactions between features -- Summary -- Chapter 5:
Introducing MLlib -- Overview of the package -- Loading
and transforming the data -- Getting to know your data --
Descriptive statistics -- Correlations -- Statistical
testing -- Creating the final dataset -- Creating an RDD
of LabeledPoints -- Splitting into training and testing --
Predicting infant survival -- Logistic regression in MLlib
-- Selecting only the most predictable features -- Random
forest in MLlib -- Summary -- Chapter 6: Introducing the
ML Package -- Overview of the package -- Transformer --
Estimators -- Classification -- Regression -- Clustering -
- Pipeline -- Predicting the chances of infant survival
with ML -- Loading the data -- Creating transformers --
Creating an estimator -- Creating a pipeline -- Fitting
the model -- Evaluating the performance of the model --
Saving the model -- Parameter hyper-tuning -- Grid search
-- Train-validation splitting -- Other features of PySpark
ML in action -- Feature extraction -- NLP -- related
feature extractors -- Discretizing continuous variables --
Standardizing continuous variables -- Classification --
Clustering -- Finding clusters in the births dataset --
Topic mining -- Regression -- Summary -- Chapter 7:
GraphFrames -- Introducing GraphFrames -- Installing
GraphFrames -- Creating a library -- Preparing your
flights dataset -- Building the graph -- Executing simple
queries -- Determining the number of airports and trips.
505 8 Determining the longest delay in this dataset --
Determining the number of delayed versus on-time/early
flights -- What flights departing Seattle are most likely
to have significant delays? -- What states tend to have
significant delays departing from Seattle? --
Understanding vertex degrees -- Determining the top
transfer airports -- Understanding motifs -- Determining
airport ranking using PageRank -- Determining the most
popular non-stop flights -- Using Breadth-First Search --
Visualizing flights using D3 -- Summary -- Chapter 8:
TensorFrames -- What is Deep Learning? -- The need for
neural networks and Deep Learning -- What is feature
engineering? -- Bridging the data and algorithm -- What is
TensorFlow? -- Installing Pip -- Installing TensorFlow --
Matrix multiplication using constants -- Matrix
multiplication using placeholders -- Running the model --
Running another model -- Discussion -- Introducing
TensorFrames -- TensorFrames -- quick start --
Configuration and setup -- Launching a Spark cluster --
Creating a TensorFrames library -- Installing TensorFlow
on your cluster -- Using TensorFlow to add a constant to
an existing column -- Executing the Tensor graph --
Blockwise reducing operations example -- Building a
DataFrame of vectors -- Analysing the DataFrame --
Computing elementwise sum and min of all vectors --
Summary -- Chapter 9: Polyglot Persistence with Blaze --
Installing Blaze -- Polyglot persistence -- Abstracting
data -- Working with NumPy arrays -- Working with pandas'
DataFrame -- Working with files -- Working with databases
-- Interacting with relational databases -- Interacting
with the MongoDB database -- Data operations -- Accessing
columns -- Symbolic transformations -- Operations on
columns -- Reducing data -- Joins -- Summary -- Chapter 10
: Structured Streaming -- What is Spark Streaming?.
505 8 Why do we need Spark Streaming? -- What is the Spark
Streaming application data flow? -- Simple streaming
application using DStreams -- A quick primer on global
aggregations -- Introducing Structured Streaming --
Summary -- Chapter 11: Packaging Spark Applications -- The
spark-submit command -- Command line parameters --
Deploying the app programmatically -- Configuring your
SparkSession -- Creating SparkSession -- Modularizing code
-- Structure of the module -- Calculating the distance
between two points -- Converting distance units --
Building an egg -- User defined functions in Spark --
Submitting a job -- Monitoring execution -- Databricks
Jobs -- Summary -- Index.
520 8 Annotation|bBuild data-intensive applications locally and
deploy at scale using the combined powers of Python and
Spark 2.0 About This Book - Learn why and how you can
efficiently use Python to process data and build machine
learning models in Apache Spark 2.0 - Develop and deploy
efficient, scalable real-time Spark solutions - Take your
understanding of using Spark with Python to the next level
with this jump start guide Who This Book Is For If you are
a Python developer who wants to learn about the Apache
Spark 2.0 ecosystem, this book is for you. A firm
understanding of Python is expected to get the best out of
the book. Familiarity with Spark would be useful, but is
not mandatory. What You Will Learn - Learn about Apache
Spark and the Spark 2.0 architecture - Build and interact
with Spark DataFrames using Spark SQL - Learn how to solve
graph and deep learning problems using GraphFrames and
TensorFrames respectively - Read, transform, and
understand data and use it to train machine learning
models - Build machine learning models with MLlib and ML -
Learn how to submit your applications programmatically
using spark-submit - Deploy locally built applications to
a cluster In Detail Apache Spark is an open source
framework for efficient cluster computing with a strong
interface for data parallelism and fault tolerance. This
book will show you how to leverage the power of Python and
put it to use in the Spark ecosystem. You will start by
getting a firm understanding of the Spark 2.0 architecture
and how to set up a Python environment for Spark. You will
get familiar with the modules available in PySpark. You
will learn how to abstract data with RDDs and DataFrames
and understand the streaming capabilities of PySpark. Also,
you will get a thorough overview of machine learning
capabilities of PySpark using ML and MLlib, graph
processing using GraphFrames, and polyglot persistence
using Blaze. Finally, you will learn how to deploy your
applications to the cloud using the spark-submit command.
By the end of this book, you will have established a firm
understanding of the Spark Python API and how it can be
used to build data-intensive applications. Style and
approach This book takes a very comprehensive, step-by-
step approach so you understand how the Spark ecosystem
can be used with Python to develop efficient, scalable
solutions. Every chapter is standalone and written in a
very easy-to-understand manner, with a focus on both the
hows and the whys of each concept.
588 Description based on online resource; title from title
page (viewed March 17, 2017).
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Application software|xDevelopment.
650 0 Python (Computer program language)
650 0 SPARK (Computer program language)
650 6 Logiciels d'application|xDéveloppement.
650 6 Python (Langage de programmation)
650 7 Application software|xDevelopment|2fast
650 7 Python (Computer program language)|2fast
650 7 SPARK (Computer program language)|2fast
700 1 Lee, Denny,|eauthor.
700 1 Karau, Holden,|ewriter of foreword.
776 08 |iPrint version:|aDrabas, Tomasz.|tLearning PySpark.
|dBirmingham : Packt Publishing, ©2017
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781786463708/?ar
|zAvailable on O'Reilly for Public Libraries
938 ProQuest MyiLibrary Digital eBook Collection|bIDEB
|ncis35945158
938 EBSCOhost|bEBSC|n1477650
938 YBP Library Services|bYANK|n13522893
994 92|bJFN