Merge pull request #116729 from hrasheed-msft/patch-140

Update apache-spark-perf.md

Author: GitHubber17

articles/hdinsight/spark/apache-spark-perf.md

@@ -16,16 +16,16 @@ This article provides an overview of strategies to optimize Apache Spark jobs on
 
 The performance of your Apache Spark jobs depends on multiple factors. These performance factors include: how your data is stored, how the cluster is configured, and the operations that are used when processing the data.
 
-Common challenges you might face include memory constraints due to improperly sized executors, long-running operations, and tasks that result in Cartesian operations.
+Common challenges you might face include: memory constraints due to improperly sized executors, long-running operations, and tasks that result in cartesian operations.
 
-There are also various strategies that can help you overcome these challenges, such as caching, and allowing for data skew.
+There are also many optimizations that can help you overcome these challenges, such as caching, and allowing for data skew.
 
-In each of the following articles, you can find common challenges and solutions for a different aspect of spark optimization.
+In each of the following articles, you can find information on different aspects of Spark optimization.
 
-* [Optimize data storage](optimize-data-storage.md)
-* [Optimize data processing](optimize-data-processing.md)
-* [Optimize memory usage](optimize-memory-usage.md)
-* [Optimize cluster configuration](optimize-cluster-configuration.md)
+* [Optimize data storage for Apache Spark](optimize-data-storage.md)
+* [Optimize data processing for Apache Spark](optimize-data-processing.md)
+* [Optimize memory usage for Apache Spark](optimize-memory-usage.md)
+* [Optimize HDInsight cluster configuration for Apache Spark](optimize-cluster-configuration.md)
 
 ## Next steps