typo

Author: mamccrea

articles/stream-analytics/streaming-technologies.md

@@ -11,11 +11,11 @@ ms.date: 05/09/2019
 
 # Choose a real-time analytics and streaming processing technology on Azure
 
-There are several options available for real-time analytics and streaming processing on Azure. This article provides the information you need in order to decide which technology is the best fit for your application.
+There are several options available for real-time analytics and streaming processing on Azure. This article provides the information you need to decide which technology is the best fit for your application.
 
 ## When to use Azure Stream Analytics
 
-Azure Stream Analytics is the recommended service for stream analytics on Azure. It is meant for a wide range of scenarios that include but are not limited to:
+Azure Stream Analytics is the recommended service for stream analytics on Azure. It's meant for a wide range of scenarios that include but aren't limited to:
 
 * Dashboards for data visualization
 * Real-time alerts from temporal and spatial patterns or anomalies
@@ -29,21 +29,21 @@ Azure Stream Analytics has a rich out-of-the-box experience. You can immediately
 
 * Built-in temporal operators, such as windowed aggregates, temporal joins, and temporal analytic functions.
 * Native Azure [input](stream-analytics-add-inputs.md) and [output](stream-analytics-define-outputs.md) adapters
-* Support for slow changing [reference data](stream-analytics-use-reference-data.md) (also known as a look up tables), including joining with geospatial reference data for geofencing.
+* Support for slow changing [reference data](stream-analytics-use-reference-data.md) (also known as a lookup tables), including joining with geospatial reference data for geofencing.
 * Integrated solutions, such as [Anomaly Detection](stream-analytics-machine-learning-anomaly-detection.md)
 * Multiple time windows in the same query
 * Ability to compose multiple temporal operators in arbitrary sequences.
-* Under 100 ms end-to-end latency from input arriving at Event Hubs, to output landing in Event Hubs, including the network delay from and to Event Hubs, at sustained high throughput
+* Under 100-ms end-to-end latency from input arriving at Event Hubs, to output landing in Event Hubs, including the network delay from and to Event Hubs, at sustained high throughput
 
 ## When to use other technologies
 
 ### You need to input from or output to Kafka
 
-Azure Stream Analytics does not have an Apache Kafka input or output adapter. If you have events landing in or need to send to Kafka and you do not have a requirement to run your own Kafka cluster, you can continue to use Stream Analytics by sending events to Event Hubs using the Event Hubs Kafka API without changing the event sender. If you do need to run your own Kafka cluster, you can use Spark Structured Streaming or Storm on HDInsight.
+Azure Stream Analytics doesn't have an Apache Kafka input or output adapter. If you have events landing in or need to send to Kafka and you don't have a requirement to run your own Kafka cluster, you can continue to use Stream Analytics by sending events to Event Hubs using the Event Hubs Kafka API without changing the event sender. If you do need to run your own Kafka cluster, you can use Spark Structured Streaming or Storm on HDInsight.
 
 ### You want to write UDFs, UDAs, and custom deserializers in a language other than JavaScript or C#
 
-Azure Stream Analytics supports user-defined functions (UDF) or user-defined aggregates (UDA) in JavaScript for cloud jobs and C# for IoT Edge jobs. C# user-defined deserializers are also supported. If you want  implement a deserializer, a UDF, or a UDA in other languages, such as Java or Python, you can use Spark Structured Streaming. You can slo run the Event Hubs **EventProcessorHost** on your own virtual machines to perform arbitrary streaming processing.
+Azure Stream Analytics supports user-defined functions (UDF) or user-defined aggregates (UDA) in JavaScript for cloud jobs and C# for IoT Edge jobs. C# user-defined deserializers are also supported. If you want to implement a deserializer, a UDF, or a UDA in other languages, such as Java or Python, you can use Spark Structured Streaming. You can also run the Event Hubs **EventProcessorHost** on your own virtual machines to do arbitrary streaming processing.
 
 ### Your solution is in a multi-cloud or on-premises environment