This course includes
Course Overview
Pandas is a popular Python library used to manipulate data, but it has certain limitations in its ability to process large datasets. The Apache Spark analytics library offers significant performance improvements. This course will help improve your Python-based data processing by leveraging Apache Spark’s multithreading capabilities through the PySpark library. You’ll start by reading data into a PySpark DataFrame before performing basic input/output functions, such as renaming attributes, selecting, and wr...Show More
What You'll Learn
A working knowledge of Apache Spark and the PySpark library for Python
A strong understanding of the advantages of using PySpark instead of Pandas for processing large datasets
The ability to calculate some Metrics or produce aggregated analytics reporting solutions
The ability to write Production Code in PySpark
What You'll Learn
A working knowledge of Apache Spark and the PySpark library for Python
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Course Content
Introduction
Data Input/Output
Data Transformation
User Defined Function (UDF)
Wrapping Up
Course Author
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Anthony Walker
@_webarchitect_
Evan Dunbar
ML Engineer
Carlos Matias La Borde
Software Developer
Souvik Kundu
Front-end Developer
Vinay Krishnaiah
Software Developer
Eric Downs
Musician/Entrepeneur
Kenan Eyvazov
DevOps Engineer
Souvik Kundu
Front-end Developer
Eric Downs
Musician/Entrepeneur
Anthony Walker
@_webarchitect_
Evan Dunbar
ML Engineer
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