Introduction to Spark with sparklyr in R
Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.
Start Course for Free4 Hours4 Videos50 Exercises15,530 Learners
4600 XPCreate Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.Loved by learners at thousands of companies
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 <code>sparklyr</code> package lets you write <code>dplyr</code> 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 <code>dplyr</code> 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
Light My Fire: Starting To Use Spark With dplyr Syntax
FreeIn 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 thedplyrinterface 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 usesparklyr's machine learning routines, by predicting the year in which a song was released.
Datasets
Anti-joinBoth-model-responsesGbt-model-responsesInner-joinLeft-joinPredicted vs actualResidual densitySemi-joinTimbreTimbre parquetTitle text parquetTrack data parquetTrack data to model parquetTrack data to predict parquetTrack metadata
Collaborators
Tom JeonPrerequisites
Intermediate R
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.
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
Join over 7 million learners and start Introduction to Spark with sparklyr in R today!
Create Your Free Account
or
By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.