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Introduction to Spark with sparklyr in R
4.7(82)20k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
The sparklyr package bridges R's expressive data manipulation syntax with Apache Spark's high-performance distributed computing backend. This intermediate course demonstrates how to run familiar dplyr pipelines on Spark clusters and execute distributed machine learning algorithms effortlessly.
Who It's For
R programmers, statisticians, and data analysts who need to scale their data cleaning, visualization, and modeling workflows to massive big data datasets.
Key Takeaways
- Spark & R Integration: Connect R sessions to Spark clusters using sparklyr and load external data sources into Spark DataFrames.
- dplyr Interface for Spark: Filter, mutate, arrange, and aggregate Spark DataFrames using clean, familiar dplyr syntax.
- Distributed Machine Learning: Train machine learning models using Spark MLlib via sparklyr, including Random Forests and Gradient Boosted Trees.
About this course
Explore the Advantages of R, Spark, and sparklyr
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 4-hour 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.Load Data into Spark and Manipulate Spark DataFrames
You’ll start this Spark course by investigating how Spark and R work well together and practicing loading data, ready for cleaning, transformation, and analysis. You’ll use Spark frames and dplyr syntax to manipulate your data by filtering and arranging rows, and mutating and summarizing columns.Delve into Big Data Analysis with Spark MLib
This course focuses on building your skills and confidence in analyzing huge datasets. The final chapters take you through Spark’s machine learning data transformation features and offer you the chance to practice sparklyr’s machine learning routines by using it to make predictions using gradient boosted trees and random forests. "Advertisement