Apache Spark Building Blocks. A high-level overview of Spark describes what is available for the R user.
SparkContext, SQLContext, and SparkSession. In Spark 1.x, SparkContext and SQLContext let you access Spark. In Spark 2.x, SparkSession becomes the primary method.
A DataFrame or a data.frame? Spark’s distributed DataFrame is different from R’s local data.frame. Knowing the differences lets you avoid simple mistakes.
Distributed Processing 101. Understanding the mechanics of Big Data processing helps you write efficient code—and not blow up your cluster’s master node.
Function Masking. Like all R libraries, SparkR masks some functions.
Specifying Rows. With Big Data and Spark, you generally select rows in DataFrames differently than in local R data.frames.
Sampling. Sample data in the right way, and use it as a tool for converting between big and small data.
Machine Learning. SparkR has a growing library of distributed ML algorithms.
Visualization.It can be hard to visualize big data, but there are tricks and tools which help.
Understanding Error Messages. For R users, Spark error messages can be daunting. Knowing how to parse them helps you find the relevant parts.
I highly recommend checking out the notebook.