edit pass: improve-stream-analytics-acrolinx-scores

Author: ShawnJackson

articles/stream-analytics/stream-analytics-real-time-event-processing-reference-architecture.md

@@ -1,30 +1,37 @@
 ---
-title: Real-time event processing using Azure Stream Analytics
-description: This article describes the reference architecture to achieve real-time event processing and analytics using Azure Stream Analytics.
+title: Real-time event processing with Azure Stream Analytics
+description: This article describes the reference architecture to achieve real-time event processing and analytics by using Azure Stream Analytics.
 
 ms.service: stream-analytics
 ms.topic: conceptual
 ms.date: 01/24/2017
 ---
 # Reference architecture: Real-time event processing with Microsoft Azure Stream Analytics
-The reference architecture for real-time event processing with Azure Stream Analytics is intended to provide a generic blueprint for deploying a real-time platform as a service (PaaS) stream-processing solution with Microsoft Azure.
+
+The reference architecture for real-time event processing with Azure Stream Analytics provides a generic blueprint for deploying a real-time platform as a service (PaaS) stream-processing solution by using Microsoft Azure.
 
 ## Summary
-Traditionally, analytics solutions have been based on capabilities such as ETL (extract, transform, load) and data warehousing, where data is stored prior to analysis. Changing requirements, including more rapidly arriving data, are pushing this existing model to the limit. The ability to analyze data within moving streams prior to storage is one solution, and while it is not a new capability, the approach has not been widely adopted across all industry verticals. 
 
-Microsoft Azure provides an extensive catalog of analytics technologies that are capable of supporting an array of different solution scenarios and requirements. Selecting which Azure services to deploy for an end-to-end solution can be a challenge given the breadth of offerings. This paper is designed to describe the capabilities and interoperation of the various Azure services that support an event-streaming solution. It also explains some of the scenarios in which customers can benefit from this type of approach.
+Traditionally, analytics solutions are based on capabilities such as ETL (extract, transform, load) and data warehousing, where data is stored before analysis. Changing requirements, including more rapidly arriving data, are pushing this existing model to the limit.
+
+The ability to analyze data within moving streams before storage is one solution. Although it isn't a new capability, the approach hasn't been widely adopted across industry verticals.
+
+Microsoft Azure provides an extensive catalog of analytics technologies that can support an array of solution scenarios and requirements. Selecting which Azure services to deploy for an end-to-end solution can be a challenge, considering the breadth of offerings.
+
+This reference is designed to describe the capabilities and interoperation of the various Azure services that support an event-streaming solution. It also explains some of the scenarios in which customers can benefit from this type of approach.
 
 ## Contents
-* Executive Summary
-* Introduction to Real-Time Analytics
-* Value Proposition of Real-Time Data in Azure
-* Common Scenarios for Real-Time Analytics
-* Architecture and Components
-  * Data Sources
-  * Data-Integration Layer
-  * Real-time Analytics Layer
-  * Data Storage Layer
-  * Presentation / Consumption Layer
+
+* Executive summary
+* Introduction to real-time analytics
+* Value proposition of real-time data in Azure
+* Common scenarios for real-time analytics
+* Architecture and components
+  * Data sources
+  * Data integration layer
+  * Real-time analytics layer
+  * Data storage layer
+  * Presentation/consumption layer
 * Conclusion
 
 **Author:** Charles Feddersen, Solution Architect, Data Insights Center of Excellence, Microsoft Corporation
@@ -36,11 +43,13 @@ Microsoft Azure provides an extensive catalog of analytics technologies that are
 **Download:** [Real-Time Event Processing with Microsoft Azure Stream Analytics](https://download.microsoft.com/download/6/2/3/623924DE-B083-4561-9624-C1AB62B5F82B/real-time-event-processing-with-microsoft-azure-stream-analytics.pdf)
 
 ## Get help
-For further assistance, try the [Microsoft Q&A question page for Azure Stream Analytics](/answers/tags/179/azure-stream-analytics)
+
+For further assistance, try the [Microsoft Q&A page for Azure Stream Analytics](/answers/tags/179/azure-stream-analytics).
 
 ## Next steps
+
 * [Introduction to Azure Stream Analytics](stream-analytics-introduction.md)
-* [Get started using Azure Stream Analytics](stream-analytics-real-time-fraud-detection.md)
-* [Scale Azure Stream Analytics jobs](stream-analytics-scale-jobs.md)
-* [Azure Stream Analytics Query Language Reference](/stream-analytics-query/stream-analytics-query-language-reference)
-* [Azure Stream Analytics Management REST API Reference](/rest/api/streamanalytics/)
+* [Analyze fraudulent call data with Stream Analytics and visualize results in a Power BI dashboard](stream-analytics-real-time-fraud-detection.md)
+* [Scale an Azure Stream Analytics job to increase throughput](stream-analytics-scale-jobs.md)
+* [Azure Stream Analytics Query Language reference](/stream-analytics-query/stream-analytics-query-language-reference)
+* [Azure Stream Analytics Management REST API](/rest/api/streamanalytics/)