PySpark cookbook :: over 60 recipes for implementing big data processing and analytics using Apache Spark and Python /
Annotation
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Annotation |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788834254 1788834259 1788835360 9781788835367 |
Internformat
MARC
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| 520 | 8 | |a Annotation |b Combine the power of Apache Spark and Python to build effective big data applicationsKey FeaturesPerform effective data processing, machine learning, and analytics using PySparkOvercome challenges in developing and deploying Spark solutions using PythonExplore recipes for efficiently combining Python and Apache Spark to process dataBook DescriptionApache 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. What you will learnConfigure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environmentsCreate DataFrames from JSON and a dictionary using pyspark.sqlExplore regression and clustering models available in the ML moduleUse DataFrames to transform data used for modelingConnect to PubNub and perform aggregations on streamsWho 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. | |
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| spelling | Lee, Denny, author. PySpark cookbook : over 60 recipes for implementing big data processing and analytics using Apache Spark and Python / Denny Lee, Tomasz Drabas. Birmingham, UK : Packt Publishing, 2018. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Online resource; title from title page (Safari, viewed July 30, 2018). Annotation Combine the power of Apache Spark and Python to build effective big data applicationsKey FeaturesPerform effective data processing, machine learning, and analytics using PySparkOvercome challenges in developing and deploying Spark solutions using PythonExplore recipes for efficiently combining Python and Apache Spark to process dataBook DescriptionApache 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. What you will learnConfigure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environmentsCreate DataFrames from JSON and a dictionary using pyspark.sqlExplore regression and clustering models available in the ML moduleUse DataFrames to transform data used for modelingConnect to PubNub and perform aggregations on streamsWho 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. Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 SPARK (Computer program language) http://id.loc.gov/authorities/subjects/sh2015001170 Logiciels d'application Développement. Python (Langage de programmation) COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Application software Development fast Python (Computer program language) fast SPARK (Computer program language) fast Drabas, Tomasz, author. has work: PYSPARK COOKBOOK (Text) https://id.oclc.org/worldcat/entity/E39PCXQ7hPQ8wHBTf4VfGHbPgq https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1841878 Volltext |
| spellingShingle | Lee, Denny Drabas, Tomasz PySpark cookbook : over 60 recipes for implementing big data processing and analytics using Apache Spark and Python / Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 SPARK (Computer program language) http://id.loc.gov/authorities/subjects/sh2015001170 Logiciels d'application Développement. Python (Langage de programmation) COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Application software Development fast Python (Computer program language) fast SPARK (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh95009362 http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh2015001170 |
| 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_full | PySpark cookbook : over 60 recipes for implementing big data processing and analytics using Apache Spark and Python / Denny Lee, Tomasz Drabas. |
| title_fullStr | PySpark cookbook : over 60 recipes for implementing big data processing and analytics using Apache Spark and Python / Denny Lee, Tomasz Drabas. |
| title_full_unstemmed | PySpark cookbook : over 60 recipes for implementing big data processing and analytics using Apache Spark and Python / Denny Lee, Tomasz Drabas. |
| 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 | Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 SPARK (Computer program language) http://id.loc.gov/authorities/subjects/sh2015001170 Logiciels d'application Développement. Python (Langage de programmation) COMPUTERS Computer Literacy. bisacsh COMPUTERS Computer Science. bisacsh COMPUTERS Data Processing. bisacsh COMPUTERS Hardware General. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS Machine Theory. bisacsh COMPUTERS Reference. bisacsh Application software Development fast Python (Computer program language) fast SPARK (Computer program language) fast |
| topic_facet | Application software Development. Python (Computer program language) SPARK (Computer program language) Logiciels d'application Développement. Python (Langage de programmation) COMPUTERS Computer Literacy. COMPUTERS Computer Science. COMPUTERS Data Processing. COMPUTERS Hardware General. COMPUTERS Information Technology. COMPUTERS Machine Theory. COMPUTERS Reference. Application software Development |
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