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  • 1
    UID:
    gbv_1670676129
    Format: 273 p
    Edition: 1st ed.
    ISBN: 9781786463708 , 9781786466259
    Content: In this book, we will guide you through the latest incarnation of Apache Spark using Python. We will show you how to read structured and unstructured data, how to use some fundamental data types available in PySpark, build machine learning models, operate on graphs, read streaming data, and deploy your models in the cloud. Each chapter will tackle different problem, and by the end of the book we hope you will be knowledgeable enough to solve other problems we did not have space to cover here.
    Note: Includes bibliographical references and index
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    almahu_BV045142290
    Format: 1 Online-Ressource (xii, 250 Seiten) : , Illustrationen, Diagramme (überwiegend farbig).
    ISBN: 978-1-78646-625-9 , 1-78646-625-2
    Note: Description based on online resource; title from title page (viewed March 17, 2017). - Includes index. - Auf dem Cover: "Foreword by Holden Karau, principal software engineer at IBM Spark Technology Center"
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-78646-370-8
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    UID:
    almafu_9959242910502883
    Format: 1 online resource (273 pages) : , illustrations
    Edition: 1st edition
    Content: Build 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. Downloading the...
    Note: Includes index.
    Additional Edition: ISBN 1-78646-370-9
    Additional Edition: ISBN 1-78646-625-2
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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