Victoria

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

Victoria E. Guzik

Associate Data Scientist @Callisto Media

Course overview

Big data is all around us, and Spark is quickly becoming an in-demand Big Data tool that employers want to see.
In this course, you’ll learn the advantages of Apache Spark. You’ll learn concepts such as Resilient Distributed Datasets (RDDs), Spark SQL, Spark DataFrames, and the difference between pandas and Spark DataFrames.

You’ll also learn how to install Spark and PySpark, a Python API that allows you to interact with Spark using Python code. You’ll learn how to integrate PySpark with Jupyter Notebook so you can analyze large datasets.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. You’ll work with a variety of real-world datasets, including the text of Hamlet, census data, and guest data from The Daily Show.

Key skills

  • Breaking down tasks using the map-reduce framework
  • Processing and transforming larger, raw files using Spark
  • Working with large, unstructured datasets using Spark SQL and Spark DataFrames

Course outline

Analyzing Large Datasets in Spark and Map-Reduce [6 lessons]

Introduction to Spark 1h

Lesson Objectives
  • Explain the history of big data
  • Define how the RDD object works in Spark
  • Count using Spark

Project: Spark Installation and Jupyter Notebook Integration 1h

Lesson Objectives
  • Install Spark and PySpark
  • Integrate PySpark with Jupyter Notebook

Transformations and Actions 1h

Lesson Objectives
  • Read TSV files into Spark
  • Apply lambda functions over RDD objects

Challenge: Transforming Hamlet into a Data Set 1h

Lesson Objectives
  • Transform data from text files into RDD objects
  • Clean data using lambda functions

Spark DataFrames 1h

Lesson Objectives
  • Employ Spark DataFrames
  • Identify the difference between pandas and Spark DataFrames
  • Perform basic filters with Spark DataFrames

Spark SQL 1h

Lesson Objectives
  • Query Spark DataFrames using SQL
  • Employ multiple tables in Spark SQL

Projects in this course

Project: Spark Installation and Jupyter Notebook Integration

For this project, we’ll step into the role of big data engineers to set up Spark and PySpark on our local machines. We’ll integrate PySpark with Jupyter Notebook to enable interactive big data analysis.

The Dataquest guarantee

Guarantee

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

Money

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

Master skills faster with Dataquest

Go from zero to job-ready

Go from zero to job-ready

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Build your project portfolio

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

Challenge yourself with exercises

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

Showcase your path certification

Showcase your path certification

Share the evidence of your hard work with your network and potential employers.

Grow your career with
Dataquest.

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of learners recommend
Dataquest for career advancement
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Average salary boost
for learners who complete a path
Aaron

Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

Jessi

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

Victoria

Victoria E. Guzik

Associate Data Scientist at Callisto Media

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!”

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