Improved Correctness Score

Author: sreekzz

articles/hdinsight/hdinsight-streaming-at-scale-overview.md

@@ -31,7 +31,7 @@ For more information, see [What is Apache Spark Streaming?](./spark/apache-spark
 
 Although you can specify the number of nodes in your cluster during creation, you may want to grow or shrink the cluster to match the workload. All HDInsight clusters allow you to [change the number of nodes in the cluster](hdinsight-administer-use-portal-linux.md#scale-clusters). Spark clusters can be dropped with no loss of data, as all  data is stored in Azure Storage or Data Lake Storage.
 
-There are advantages to decoupling technologies. For instance, Kafka is an event buffering technology, so it's very IO intensive and doesn't need much processing power. In comparison, stream processors such as Spark Streaming are compute-intensive, requiring more powerful VMs. By having these technologies decoupled into different clusters, you can scale them independently while best utilizing the VMs.
+There are advantages to decoupling technologies. For instance, Kafka is an event buffering technology, so its very IO intensive and doesn't need much processing power. In comparison, stream processors such as Spark Streaming are compute-intensive, requiring more powerful VMs. By having these technologies decoupled into different clusters, you can scale them independently while best utilizing the VMs.
 
 ### Scale the stream buffering layer