Cleaning Data with PySpark

Learn how to clean data with Apache Spark in Python.
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4 Hours16 Videos53 Exercises7,944 Learners
4150 XP

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Course Description

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.

  1. 1

    DataFrame details

    Free
    A review of DataFrame fundamentals and the importance of data cleaning.
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  2. 2

    Manipulating DataFrames in the real world

    A look at various techniques to modify the contents of DataFrames in Spark.
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  3. 3

    Improving Performance

    Improve data cleaning tasks by increasing performance or reducing resource requirements.
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  4. 4

    Complex processing and data pipelines

    Learn how to process complex real-world data using Spark and the basics of pipelines.
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In the following tracks
Big Data with PySparkData Engineer
Collaborators
Hadrien LacroixHillary Green-Lerman
Mike Metzger Headshot

Mike Metzger

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.
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Lloyds Banking Group

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Harvard Business School

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Decision Science Analytics, USAA