PySpark Recipes
A Problem-Solution Approach with PySpark2
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Beschreibung
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
Presents advanced features of PySpark and code optimization techniques
Covers SparkSQL, Spark Streaming, Spark MLlib, and GraphFrames
Discusses and demonstrates Data Science and Big Data processing with PySpark MLlib
Presents advanced features of PySpark and code optimization techniques Covers SparkSQL, Spark Streaming, Spark MLlib, and GraphFrames Discusses and demonstrates Data Science and Big Data processing with PySpark MLlib
Autor*in
Raju Kumar Mishra
Themen in »PySpark Recipes«
Spark Python Numpy Scipy Resilient Distributed Database Advanced PySpark Spark SQL MLLIb PySpark2