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.
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.
In which you learn more about using the
dplyr interface to Spark, including advanced field selection, calculating groupwise statistics, and joining data frames.
In which you learn about Spark's machine learning data transformation features, and functionality for manipulating native DataFrames.
A case study in which you learn to use
sparklyr's machine learning routines, by predicting the year in which a song was released.
DatasetsAnti-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
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