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Cleaning Data with PySpark

Learn how to clean data with Apache Spark in Python.

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4 Hours16 Videos53 Exercises15,884 Learners4150 XPBig Data with PySpark TrackData Engineer Track

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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.

    Play Chapter Now
    Intro to data cleaning with Apache Spark
    50 xp
    Data cleaning review
    50 xp
    Defining a schema
    100 xp
    Immutability and lazy processing
    50 xp
    Immutability review
    50 xp
    Using lazy processing
    100 xp
    Understanding Parquet
    50 xp
    Saving a DataFrame in Parquet format
    100 xp
    SQL and Parquet
    100 xp
  2. 2

    Manipulating DataFrames in the real world

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

    Play Chapter Now
    DataFrame column operations
    50 xp
    Filtering column content with Python
    100 xp
    Filtering Question #1
    50 xp
    Filtering Question #2
    50 xp
    Modifying DataFrame columns
    100 xp
    Conditional DataFrame column operations
    50 xp
    when() example
    100 xp
    When / Otherwise
    100 xp
    User defined functions
    50 xp
    Understanding user defined functions
    50 xp
    Using user defined functions in Spark
    100 xp
    Partitioning and lazy processing
    50 xp
    Adding an ID Field
    100 xp
    IDs with different partitions
    100 xp
    More ID tricks
    100 xp
  3. 3

    Improving Performance

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

    Play Chapter Now
    Caching
    50 xp
    Caching a DataFrame
    100 xp
    Removing a DataFrame from cache
    100 xp
    Improve import performance
    50 xp
    File size optimization
    50 xp
    File import performance
    100 xp
    Cluster configurations
    50 xp
    Reading Spark configurations
    100 xp
    Writing Spark configurations
    100 xp
    Performance improvements
    50 xp
    Normal joins
    100 xp
    Using broadcasting on Spark joins
    100 xp
    Comparing broadcast vs normal joins
    100 xp
  4. 4

    Complex processing and data pipelines

    Learn how to process complex real-world data using Spark and the basics of pipelines.

    Play Chapter Now
    Introduction to data pipelines
    50 xp
    Quick pipeline
    100 xp
    Pipeline data issue
    50 xp
    Data handling techniques
    50 xp
    Removing commented lines
    100 xp
    Removing invalid rows
    100 xp
    Splitting into columns
    100 xp
    Further parsing
    100 xp
    Data validation
    50 xp
    Validate rows via join
    100 xp
    Examining invalid rows
    100 xp
    Final analysis and delivery
    50 xp
    Dog parsing
    100 xp
    Per image count
    100 xp
    Percentage dog pixels
    100 xp
    Congratulations and next steps
    50 xp

In the following tracks

Big Data with PySparkData Engineer

Datasets

Dallas Council VotesDallas Council VotersFlights - 2014Flights - 2015Flights - 2016Flights - 2017

Collaborators

hadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroixhillary-green-lermanHillary Green-Lerman

Prerequisites

Intermediate PythonIntroduction to PySpark
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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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).