Data analytics with Spark using Python
Spark for Data Professionals introduces and solidifies the concepts behind Spark 2.x, teaching working developers, architects, and data professionals exactly how to build practical Spark solutions. Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR,...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Boston
Addison-Wesley
[2018]
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| Schriftenreihe: | Addison-Wesley data & analytics series
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| spelling | Aven, Jeffrey VerfasserIn aut Data analytics with Spark using Python Jeffrey Aven Boston Addison-Wesley [2018] ©2018 1 online resource (1 volume) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Addison-Wesley data & analytics series Includes index. - Online resource; title from title page (Safari, viewed June 8, 2018) Spark for Data Professionals introduces and solidifies the concepts behind Spark 2.x, teaching working developers, architects, and data professionals exactly how to build practical Spark solutions. Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR, Spark Streaming, Messaging, NoSQL and Hadoop integration. Each chapter presents practical exercises deploying Spark to your local or cloud environment, plus programming exercises for building real applications. Unlike other Spark guides, Spark for Data Professionals explains crucial concepts step-by-step, assuming no extensive background as an open source developer. It provides a complete foundation for quickly progressing to more advanced data science and machine learning topics. This guide will help you: Understand Spark basics that will make you a better programmer and cluster "citizen" Master Spark programming techniques that maximize your productivity Choose the right approach for each problem Make the most of built-in platform constructs, including broadcast variables, accumulators, effective partitioning, caching, and checkpointing Leverage powerful tools for managing streaming, structured, semi-structured, and unstructured data Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Management Big data Python (Computer program language) Données volumineuses Python (Langage de programmation) Electronic data processing ; Distributed processing ; Management TUM01 ZDB-30-ORH TUM_PDA_ORH https://learning.oreilly.com/library/view/-/9780134844855/?ar X:ORHE Aggregator lizenzpflichtig Volltext |
| spellingShingle | Aven, Jeffrey Data analytics with Spark using Python Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Management Big data Python (Computer program language) Données volumineuses Python (Langage de programmation) Electronic data processing ; Distributed processing ; Management |
| title | Data analytics with Spark using Python |
| title_auth | Data analytics with Spark using Python |
| title_exact_search | Data analytics with Spark using Python |
| title_full | Data analytics with Spark using Python Jeffrey Aven |
| title_fullStr | Data analytics with Spark using Python Jeffrey Aven |
| title_full_unstemmed | Data analytics with Spark using Python Jeffrey Aven |
| title_short | Data analytics with Spark using Python |
| title_sort | data analytics with spark using python |
| topic | Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Management Big data Python (Computer program language) Données volumineuses Python (Langage de programmation) Electronic data processing ; Distributed processing ; Management |
| topic_facet | Spark (Electronic resource : Apache Software Foundation) Electronic data processing Distributed processing Management Big data Python (Computer program language) Données volumineuses Python (Langage de programmation) Electronic data processing ; Distributed processing ; Management |
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