PySpark recipes : a problem-solution approach with PySpark2 /

Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark R...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mishra, Raju Kumar (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [United States] : Apress, [2018]
Temas:
Python (Computer program language)
SPARK (Computer program language)
Computer Science.
Big Data.
Programming Techniques.
Programming Languages, Compilers, Interpreters.
Data Mining and Knowledge Discovery.
Python (Langage de programmation)
Computer programming > software development.
Programming & scripting languages: general.
Data mining.
Databases.
COMPUTERS > Programming Languages > General.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Chapter 1: The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks
  • Chapter 2: Installation
  • Chapter 3: Introduction to Python and NumPy
  • Chapter 4: Spark Architecture and Resilient Distributed Dataset
  • Chapter 5: The Power of Pairs: Paired RDD
  • Chapter 6: IO in PySpark
  • Chapter 7: Optimizing PySpark and PySpark Streaming
  • Chapter 8: PySparkSQL
  • Chapter 9: PySpark MLlib and Linear Regression.

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