Cleaning Data with PySpark
Learn how to clean data with Apache Spark in Python.Start Course for Free
4 Hours16 Videos53 Exercises20,729 Learners
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Working with data is tricky - working with millions or even billions of rows is worse. Did you receive some data processing code written on a laptop with fairly pristine data? Chances are you’ve probably been put in charge of moving a basic data process from prototype to production. You may have worked with real world datasets, with missing fields, bizarre formatting, and orders of magnitude more data. Even if this is all new to you, this course helps you learn what’s needed to prepare data processes using Python with Apache Spark. You’ll learn terminology, methods, and some best practices to create a performant, maintainable, and understandable data processing platform.
A review of DataFrame fundamentals and the importance of data cleaning.
Manipulating DataFrames in the real world
A look at various techniques to modify the contents of DataFrames in Spark.DataFrame column operations50 xpFiltering column content with Python100 xpFiltering Question #150 xpFiltering Question #250 xpModifying DataFrame columns100 xpConditional DataFrame column operations50 xpwhen() example100 xpWhen / Otherwise100 xpUser defined functions50 xpUnderstanding user defined functions50 xpUsing user defined functions in Spark100 xpPartitioning and lazy processing50 xpAdding an ID Field100 xpIDs with different partitions100 xpMore ID tricks100 xp
Improve data cleaning tasks by increasing performance or reducing resource requirements.Caching50 xpCaching a DataFrame100 xpRemoving a DataFrame from cache100 xpImprove import performance50 xpFile size optimization50 xpFile import performance100 xpCluster configurations50 xpReading Spark configurations100 xpWriting Spark configurations100 xpPerformance improvements50 xpNormal joins100 xpUsing broadcasting on Spark joins100 xpComparing broadcast vs normal joins100 xp
Complex processing and data pipelines
Learn how to process complex real-world data using Spark and the basics of pipelines.Introduction to data pipelines50 xpQuick pipeline100 xpPipeline data issue50 xpData handling techniques50 xpRemoving commented lines100 xpRemoving invalid rows100 xpSplitting into columns100 xpFurther parsing100 xpData validation50 xpValidate rows via join100 xpExamining invalid rows100 xpFinal analysis and delivery50 xpDog parsing100 xpPer image count100 xpPercentage dog pixels100 xpCongratulations and next steps50 xp
In the following tracksBig Data with PySpark
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PrerequisitesIntermediate PythonIntroduction to PySpark
Mike MetzgerSee More
Data Engineer Consultant @ Flexible Creations
Mike is a consultant focusing on data engineering and analysis using SQL, Python, and Apache Spark among other technologies. He has a 20+ year history of working with various technologies in the data, networking, and security space.
Don’t just take our word for it
*4.1from 11 reviews
- Jegan D.26 days
Very good course with challenging examples. The only problem is that I found it difficult to submit some of my answers or the solution provided. This happened in two different exercises.
- Martin G.5 months
It´s a great course, very in - deep view of topics
- Jesús M.7 months
- Hannes W.7 months
- Tolulope O.8 months
"Very good course with challenging examples. The only problem is that I found it difficult to submit some of my answers or the solution provided. This happened in two different exercises."
"It´s a great course, very in - deep view of topics"
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