Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/16856#discussion_r104255799
--- Diff: docs/quick-start.md ---
@@ -65,41 +66,41 @@ res3: Long = 15
./bin/pyspark
-Spark's primary abstraction is a distributed collection of items called a
Resilient Distributed Dataset (RDD). RDDs can be created from Hadoop
InputFormats (such as HDFS files) or by transforming other RDDs. Let's make a
new RDD from the text of the README file in the Spark source directory:
+Spark's primary abstraction is a distributed collection of items called a
Dataset. Datasets can be created from Hadoop InputFormats (such as HDFS files)
or by transforming other Datasets. Due to Python's dynamic nature, we don't
need the Dataset to be strongly typed in Python. As a result, all Datasets in
Python are Dataset[Row], and we call it `DataFrame` to be consistent with the
data frame concept in Pandas and R. Let's make a new DataFrame from the text of
the README file in the Spark source directory:
--- End diff --
strongly typed -> strongly-typed
(Same above, maybe elsewhere)
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