Interactive Course

Introduction to Spark with sparklyr in R

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.

  • 4 hours
  • 4 Videos
  • 50 Exercises
  • 14,973 Participants
  • 4,600 XP

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Course Description

R is mostly optimized to help you write data analysis code quickly and readably. Apache Spark is designed to analyze huge datasets quickly. The sparklyr package lets you write dplyr R code that runs on a Spark cluster, giving you the best of both worlds. This course teaches you how to manipulate Spark DataFrames using both the dplyr interface and the native interface to Spark, as well as trying machine learning techniques. Throughout the course, you'll explore the Million Song Dataset.

  1. 1

    Light My Fire: Starting To Use Spark With dplyr Syntax


    In which you learn how Spark and R complement each other, how to get data to and from Spark, and how to manipulate Spark data frames using dplyr syntax.

  2. Tools of the Trade: Advanced dplyr Usage

    In which you learn more about using the dplyr interface to Spark, including advanced field selection, calculating groupwise statistics, and joining data frames.

  3. Going Native: Use The Native Interface to Manipulate Spark DataFrames

    In which you learn about Spark's machine learning data transformation features, and functionality for manipulating native DataFrames.

  4. Case Study: Learning to be a Machine: Running Machine Learning Models on Spark

    A case study in which you learn to use sparklyr's machine learning routines, by predicting the year in which a song was released.

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Richie Cotton
Richie Cotton

Curriculum Architect at DataCamp

Richie runs the Content Quality team at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.

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