PySpark Recipes
A Problem-Solution Approach with PySpark2
Published on 10. December 2017
Book
Paperback/Softback
XXIII, 265 pages
978-1-4842-3140-1 (ISBN)
Description
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
Data analysts, Python programmers, big data enthusiasts
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
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Understand the advanced features of PySpark2 and SparkSQL
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Optimize your code
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Program SparkSQL with Python
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Use Spark Streaming and Spark MLlib with Python
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Perform graph analysis with GraphFrames
Data analysts, Python programmers, big data enthusiasts
More details
Edition
1st ed.
Language
English
Place of publication
Berkeley
United States
Target group
Professional and scholarly
Illustrations
35 s/w Abbildungen, 12 farbige Abbildungen
XXIII, 265 p. 47 illus., 12 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
446 gr
ISBN-13
978-1-4842-3140-1 (9781484231401)
DOI
10.1007/978-1-4842-3141-8
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2017
APress
€62.99
Available for download
Person
Raju Mishra
has strong interests in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue an M. Tech in computational sciences from Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its different applications. Working as a corporate trainer he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant solving complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others.
Content
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.