Learning PySpark

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Packt Publishing Ltd, 27. 2. 2017. - 274 страница

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

About This BookLearn why and how you can efficiently use Python to process data and build machine learning models in Apache Spark 2.0Develop and deploy efficient, scalable real-time Spark solutionsTake your understanding of using Spark with Python to the next level with this jump start guideWho 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 LearnLearn about Apache Spark and the Spark 2.0 architectureBuild and interact with Spark DataFrames using Spark SQLLearn how to solve graph and deep learning problems using GraphFrames and TensorFrames respectivelyRead, transform, and understand data and use it to train machine learning modelsBuild machine learning models with MLlib and MLLearn how to submit your applications programmatically using spark-submitDeploy locally built applications to a clusterIn 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.

 

Изабране странице

Садржај

Understanding Spark
1
Resilient Distributed Datasets
17
DataFrames
33
Prepare Data for Modeling
55
Introducing MLlib
79
Introducing the ML Package
97
GraphFrames
131
TensorFrames
157
Polyglot Persistence with Blaze
183
Structured Streaming
203
Packaging Spark Applications
223
Index
243
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Learning Pyspark
Tomasz Drabas,Denny Lee
Приказ није доступан - 2017

Чести термини и фразе

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О аутору (2017)

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 13 years of experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting he gained while working on three continents: Europe, Australia, and North America. While in Australia, Tomasz has been working on his PhD in Operations Research with a focus on choice modeling and revenue management applications in the airline industry. At Microsoft, Tomasz works with big data on a daily basis, solving machine learning problems such as anomaly detection, churn prediction, and pattern recognition using Spark. Tomasz has also authored the Practical Data Analysis Cookbook published by Packt Publishing in 2016.

Denny Lee is a Principal Program Manager at Microsoft for the Azure DocumentDB team—Microsoft's blazing fast, planet-scale managed document store service. He is a hands-on distributed systems and data science engineer with more than 18 years of experience developing Internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He has extensive experience of building greenfield teams as well as turnaround/ change catalyst. Prior to joining the Azure DocumentDB team, Denny worked as a Technology Evangelist at Databricks; he has been working with Apache Spark since 0.5. He was also the Senior Director of Data Sciences Engineering at Concur, and was on the incubation team that built Microsoft's Hadoop on Windows and Azure service (currently known as HDInsight). Denny also has a Masters in Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise healthcare customers for the last 15 years.

Библиографски подаци

НасловLearning PySpark
АуториTomasz Drabas, Denny Lee
ИздавачPackt Publishing Ltd, 2017
ISBN1786466252, 9781786466259
Дужина274 страница
  
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