Paperback. Condizione: Good.
Condizione: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
EUR 42,89
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Aggiungi al carrelloCondizione: New.
Da: California Books, Miami, FL, U.S.A.
EUR 45,24
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Aggiungi al carrelloCondizione: New.
EUR 45,78
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Packt Publishing Limited, GB, 2023
ISBN 10: 1788835360 ISBN 13: 9781788835367
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 54,27
Quantità: Più di 20 disponibili
Aggiungi al carrelloDigital. Condizione: New. Combine the power of Apache Spark and Python to build effective big data applicationsAbout This Book. Perform effective data processing, machine learning, and analytics using PySpark. Overcome challenges in developing and deploying Spark solutions using Python. Explore recipes for efficiently combining Python and Apache Spark to process dataWho This Book Is ForThe PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.What You Will Learn. Configure a local instance of PySpark in a virtual environment . Install and configure Jupyter in local and multi-node environments. Create DataFrames from JSON and a dictionary using pyspark.sql. Explore regression and clustering models available in the ML module. Use DataFrames to transform data used for modeling. Connect to PubNub and perform aggregations on streamsIn DetailApache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.You'll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You'll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you'll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You'll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.Style and approachThis book is a rich collection of recipes that will come in handy when you are working with PySparkAddressing your common and not-so-common pain points, this is a book that you must have on the shelf.
Condizione: New. pp. 330.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 48,29
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Aggiungi al carrelloCondizione: New. In English.
Lingua: Inglese
Editore: Packt Publishing 2018-06-29, 2018
ISBN 10: 1788835360 ISBN 13: 9781788835367
Da: Chiron Media, Wallingford, Regno Unito
EUR 44,46
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Aggiungi al carrelloPaperback. Condizione: New.
EUR 47,40
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Aggiungi al carrelloCondizione: New.
EUR 52,74
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 79,77
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. New. book.
Lingua: Inglese
Editore: Packt Publishing Limited, GB, 2023
ISBN 10: 1788835360 ISBN 13: 9781788835367
Da: Rarewaves.com UK, London, Regno Unito
EUR 50,36
Quantità: Più di 20 disponibili
Aggiungi al carrelloDigital. Condizione: New. Combine the power of Apache Spark and Python to build effective big data applicationsAbout This Book. Perform effective data processing, machine learning, and analytics using PySpark. Overcome challenges in developing and deploying Spark solutions using Python. Explore recipes for efficiently combining Python and Apache Spark to process dataWho This Book Is ForThe PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.What You Will Learn. Configure a local instance of PySpark in a virtual environment . Install and configure Jupyter in local and multi-node environments. Create DataFrames from JSON and a dictionary using pyspark.sql. Explore regression and clustering models available in the ML module. Use DataFrames to transform data used for modeling. Connect to PubNub and perform aggregations on streamsIn DetailApache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem.You'll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You'll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you'll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You'll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command.By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications.Style and approachThis book is a rich collection of recipes that will come in handy when you are working with PySparkAddressing your common and not-so-common pain points, this is a book that you must have on the shelf.
Da: PBShop.store US, Wood Dale, IL, U.S.A.
EUR 53,31
Quantità: Più di 20 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 48,73
Quantità: Più di 20 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Da: Majestic Books, Hounslow, Regno Unito
EUR 52,86
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. Print on Demand pp. 330.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 53,71
Quantità: 4 disponibili
Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 330.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 56,93
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Da: moluna, Greven, Germania
EUR 56,43
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This cookbook presents recipes on leveraging the power of Python and putting it to use in the Apache Spark ecosystem. By the end of this book, you will be able to solve any problem associated with building effective, data-intensive applications and performi.
Da: preigu, Osnabrück, Germania
EUR 58,55
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. PySpark Cookbook | Tomasz Drabas (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2018 | Packt Publishing | EAN 9781788835367 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 68,63
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.