links

Author: mamccrea

articles/stream-analytics/streaming-technologies.md

@@ -6,28 +6,28 @@ ms.author: zhongc
 ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 05/09/2019
+ms.date: 05/15/2019
 ---
 
 # 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 to decide which technology is the best fit for your application.
+There are several services 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'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
+* Real-time [alerts](stream-analytics-set-up-alerts.md) from temporal and spatial patterns or anomalies
 * Extract, Transform, Load (ETL)
 * [Event Sourcing pattern](/azure/architecture/patterns/event-sourcing.md)
-* IoT Edge
+* [IoT Edge](stream-analytics-edge.md)
 
 Adding an Azure Stream Analytics job to your application is the fastest way to get streaming analytics up and running in Azure, using the SQL language you already know. Azure Stream Analytics is a job service, so you don't have to spend time managing clusters, and you don't have to worry about downtime with a 99.9% SLA at the job level. Billing is also done at the job level making startup costs low (one Streaming Unit), but scalable (up to 192 Streaming Units). It's much more cost effective to run a few Stream Analytics jobs than it is to run and maintain a cluster.
 
 Azure Stream Analytics has a rich out-of-the-box experience. You can immediately take advantage of the following features without any additional setup:
 
-* Built-in temporal operators, such as windowed aggregates, temporal joins, and temporal analytic functions.
+* Built-in temporal operators, such as [windowed aggregates](stream-analytics-window-functions.md), 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 lookup tables), including joining with geospatial reference data for geofencing.
 * Integrated solutions, such as [Anomaly Detection](stream-analytics-machine-learning-anomaly-detection.md)
@@ -39,7 +39,7 @@ Azure Stream Analytics has a rich out-of-the-box experience. You can immediately
 
 ### You need to input from or output to Kafka
 
-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.
+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, which is fully supported on [Azure Databricks](../azure-databricks/index.yml), or Storm on [Azure HDInsight](../hdinsight/storm/apache-storm-tutorial-get-started-linux.md).
 
 ### You want to write UDFs, UDAs, and custom deserializers in a language other than JavaScript or C#