Learning PySpark : Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0
Language: English
Published by Packt Publishing, 2017
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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£ 68.57
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nach der Bestellung gedruckt Neuware - Printed after ordering - Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0Key Features:Learn 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 guideBook Description: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.What You Will Learn:Learn 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 clusterWho 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.…
Seller Inventory # 9781786463708
- Title
- Learning PySpark : Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0
- Author
- Tomasz Drabas
- Publisher
- Packt Publishing
- Publication year
- 2017
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1786463709
- ISBN 13
- 9781786463708
- Item weight
- 518 grams
- Dimensions
- 235x191x15 mm
Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0
Key Features:
- 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
Book Description:
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.
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
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.
"Synopsis" may belong to another edition of this title.
About the Author
Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.
"About the title" may belong to another edition of this title.
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