update vs spark doc (apache#3116)

* update vs spark doc

* update based on comments

docs/content/comparisons/druid-vs-spark.md

@@ -7,15 +7,18 @@ Druid vs Spark
 
 Druid and Spark are complementary solutions as Druid can be used to accelerate OLAP queries in Spark.
 
-Spark is a cluster computing framework built around the concept of Resilient Distributed Datasets (RDDs) and
-can be viewed as a back-office analytics platform.  RDDs enable data reuse by persisting intermediate results
+Spark is a general cluster computing framework initially designed around the concept of Resilient Distributed Datasets (RDDs). 
+RDDs enable data reuse by persisting intermediate results 
 in memory and enable Spark to provide fast computations for iterative algorithms.
 This is especially beneficial for certain work flows such as machine
 learning, where the same operation may be applied over and over
-again until some result is converged upon.  Spark provides analysts with
-the ability to run queries and analyze large amounts of data with a
-wide array of different algorithms.
+again until some result is converged upon. The generality of Spark makes it very suitable as an engine to process (clean or transform) data. 
+Although Spark provides the ability to query data through Spark SQL, much like Hadoop, the query latencies are not specifically targeted to be interactive (sub-second).
 
-Druid is designed to power analytic applications and focuses on the latencies to ingest data and serve queries
-over that data. If you were to build an application where users could
-arbitrarily explore data, the latencies seen by using Spark will likely be too slow for an interactive experience.
+Druid's focus is on extremely low latency queries, and is ideal for powering applications used by thousands of users, and where each query must 
+return fast enough such that users can interactively explore through data. Druid fully indexes all data, and can act as a middle layer between Spark and your application. 
+One typical setup seen in production is to process data in Spark, and load the processed data into Druid for faster access.
+
+For more information about using Druid and Spark together, including benchmarks of the two systems, please see:
+
+<https://www.linkedin.com/pulse/combining-druid-spark-interactive-flexible-analytics-scale-butani>