PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python
bCombine the power of Apache Spark and Python to build effective big data applications/bh2About This Book/h2ulliPerform effective data processing, machine learning, and analytics using PySpark/liliOvercome challenges in developing and deploying Spark solutions using Python/liliExplore recipes for ef...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2018
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bCombine the power of Apache Spark and Python to build effective big data applications/bh2About This Book/h2ulliPerform effective data processing, machine learning, and analytics using PySpark/liliOvercome challenges in developing and deploying Spark solutions using Python/liliExplore recipes for efficiently combining Python and Apache Spark to process data/li/ulh2Who This Book Is For/h2The 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.h2What You Will Learn/h2ulliConfigure a local instance of PySpark in a virtual environment/liliInstall and configure Jupyter in local and multi-node environments/liliCreate DataFrames from JSON and a dictionary using pyspark.sql/liliExplore regression and clustering models available in the ML module/liliUse DataFrames to transform data used for modeling/liliConnect to PubNub and perform aggregations on streams/li/ulh2In Detail/h2Apache 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.h2Style and approach/h2This 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 |
| Beschreibung: | 1 Online-Ressource (330 Seiten) |
| ISBN: | 9781788834254 |
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| 520 | |a bCombine the power of Apache Spark and Python to build effective big data applications/bh2About This Book/h2ulliPerform effective data processing, machine learning, and analytics using PySpark/liliOvercome challenges in developing and deploying Spark solutions using Python/liliExplore recipes for efficiently combining Python and Apache Spark to process data/li/ulh2Who This Book Is For/h2The 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. | ||
| 520 | |a A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.h2What You Will Learn/h2ulliConfigure a local instance of PySpark in a virtual environment/liliInstall and configure Jupyter in local and multi-node environments/liliCreate DataFrames from JSON and a dictionary using pyspark.sql/liliExplore regression and clustering models available in the ML module/liliUse DataFrames to transform data used for modeling/liliConnect to PubNub and perform aggregations on streams/li/ulh2In Detail/h2Apache 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. | ||
| 520 | |a 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.h2Style and approach/h2This 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 | ||
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| spelling | Lee, Denny Verfasser aut PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python Lee, Denny 1 Birmingham Packt Publishing Limited 2018 1 Online-Ressource (330 Seiten) txt rdacontent c rdamedia cr rdacarrier bCombine the power of Apache Spark and Python to build effective big data applications/bh2About This Book/h2ulliPerform effective data processing, machine learning, and analytics using PySpark/liliOvercome challenges in developing and deploying Spark solutions using Python/liliExplore recipes for efficiently combining Python and Apache Spark to process data/li/ulh2Who This Book Is For/h2The 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.h2What You Will Learn/h2ulliConfigure a local instance of PySpark in a virtual environment/liliInstall and configure Jupyter in local and multi-node environments/liliCreate DataFrames from JSON and a dictionary using pyspark.sql/liliExplore regression and clustering models available in the ML module/liliUse DataFrames to transform data used for modeling/liliConnect to PubNub and perform aggregations on streams/li/ulh2In Detail/h2Apache 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.h2Style and approach/h2This 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 COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Python Drabas, Tomasz Sonstige oth |
| spellingShingle | Lee, Denny PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Python |
| title | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python |
| title_auth | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python |
| title_exact_search | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python |
| title_exact_search_txtP | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python |
| title_full | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python Lee, Denny |
| title_fullStr | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python Lee, Denny |
| title_full_unstemmed | PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python Lee, Denny |
| title_short | PySpark Cookbook |
| title_sort | pyspark cookbook over 60 recipes for implementing big data processing and analytics using apache spark and python |
| title_sub | Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python |
| topic | COMPUTERS / Data Modeling & Design COMPUTERS / Programming Languages / Python |
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