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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[United States] :
Apress,
[2018]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Mishra, Raju Kumar, |e author. | |
| 245 | 1 | 0 | |a PySpark recipes : |b a problem-solution approach with PySpark2 / |c Raju Kumar Mishra. |
| 264 | 1 | |a [United States] : |b Apress, |c [2018] | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |b PDF | ||
| 347 | |a text file | ||
| 500 | |a Includes index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed December 20, 2017). | |
| 505 | 0 | |a 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. | |
| 520 | |6 880-01 |a 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 Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model. What You Will Learn: Understand the advanced features of PySpark and SparkSQL Optimize your code Program SparkSQL with Python Use Spark Streaming and Spark MLlib with Python Perform graph analysis with GraphFrames. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a SPARK (Computer program language) | |
| 650 | 1 | 4 | |a Computer Science. |
| 650 | 2 | 4 | |a Big Data. |
| 650 | 2 | 4 | |a Programming Techniques. |
| 650 | 2 | 4 | |a Programming Languages, Compilers, Interpreters. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Computer programming |x software development. |2 bicssc | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Data mining. |2 bicssc | |
| 650 | 7 | |a Databases. |2 bicssc | |
| 650 | 7 | |a COMPUTERS |x Programming Languages |x General. |2 bisacsh | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a SPARK (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Mishra, Raju Kumar. |t PySpark recipes. |d [United States] : Apress, [2018] |z 1484231406 |z 9781484231401 |w (OCoLC)1004042658 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484231418/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 880 | 0 | |6 505-00/Thai |a 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: ๓park MLlib and Linear Regression. | |
| 880 | |6 520-01/$1 |a Quickly find solutions to common programming problems宣ountered 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 Recipes㯶ers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model. What You Will Learn: ծderstand the advanced features of PySpark and SparkSQL Optimize your code Program SparkSQL with Python Use Spark Streaming and Spark MLlib with Python Perform graph analysis with GraphFrames. | ||
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