Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA

Modeling big data with R, sparklyr, and Apache Spark

John Mount (Win-Vector LLC)
1:30pm5:00pm Tuesday, March 14, 2017
Data science & advanced analytics
Location: LL21 C/D Level: Intermediate
Secondary topics:  R

Who is this presentation for?

  • Data scientists, data analysts, modelers, R users, Spark users, statisticians, and those in IT

Prerequisite knowledge

  • Basic familiarity with R
  • Experience using the dplyr R package (If you have not used dplyr before, please read this chapter before coming to class.)

Materials or downloads needed in advance

  • A WiFi-enabled laptop (You'll be provided an RStudio Server Pro login to use on the day of the workshop.)

What you'll learn

  • Learn how to quickly set up a local Spark instance, store big data in Spark and then connect to the data with R, use R to apply machine-learning algorithms to big data stored in Spark, and filter and aggregate big data stored in Spark and then import the results into R for analysis and visualization
  • Understand how to extend R (sparklyr) to access the entire Spark API

Description

Sparklyr, developed by RStudio in conjunction with IBM, Cloudera, and H2O, provides an R interface to Spark’s distributed machine-learning algorithms and much more. Sparklyr makes practical machine learning scalable and easy. With sparklyr, you can interactively manipulate Spark data using both dplyr and SQL (via DBI); filter and aggregate Spark datasets then bring them into R for analysis and visualization; orchestrate distributed machine learning from R using either Spark MLlib or H2O SparkingWater; create extensions that call the full Spark API and provide interfaces to Spark packages; and establish Spark connections and browse Spark data frames within the RStudio IDE.

John Mount demonstrates how to use sparklyr to analyze big data in Spark, covering filtering and manipulating Spark data to import into R and using R to run machine-learning algorithms on data in Spark. John also also explores the sparklyr integration built into the RStudio IDE.

Photo of John Mount

John Mount

Win-Vector LLC

John Mount is a principal consultant at Win-Vector LLC, a San Francisco data science consultancy. John has worked as a computational scientist in biotechnology and a stock-trading algorithm designer and has managed a research team for Shopping.com (now an eBay company). He is the coauthor of Practical Data Science with R (Manning Publications, 2014). John started his advanced education in mathematics at UC Berkeley and holds a PhD in computer science from Carnegie Mellon (specializing in the design and analysis of randomized algorithms). He currently blogs about technical issues at the Win-Vector blog, tweets at @WinVectorLLC, and is active in the Rotary. Please contact jmount@win-vector.com for projects and collaborations.

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