Manage code with notebooks and Databricks ReposĪzure Databricks notebooks support R. This article describes how to use R, SparkR, sparklyr, and dplyr to work with R ames, Spark DataFrames, and Spark tables in Azure Databricks. Work with DataFrames and tables with SparkR and sparklyr This article explains key similarities and differences between SparkR and sparklyr. sparklyr is an R interface to Apache Spark that provides functionality similar to dplyr, broom, and DBI. This article provides an introduction to sparklyr. SparkR supports operations like selection, filtering, and aggregation (similar to R data frames) but on large datasets. SparkR is an R interface to Apache Spark that provides a distributed data frame implementation. These articles provide an introduction and reference for SparkR. The following subsections list key features and tips to help you begin developing in Azure Databricks with R.Īzure Databricks supports two APIs that provide an R interface to Apache Spark: SparkR and sparklyr. See Import a notebook for instructions on importing notebook examples into your workspace. The following tutorials provide example code and notebooks to learn about common workflows. Use machine learning to analyze your data.
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