seo updates

Author: mamccrea

articles/stream-analytics/media/stream-analytics-high-frequency-trading/pnl.png renamed to articles/stream-analytics/media/stream-analytics-high-frequency-trading/pnl-power-bi-chart.png

@@ -4,11 +4,10 @@ description: This article describes how to use Machine Language user defined fun
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 03/28/2017
+ms.date: 12/07/2018
 ---
 # Machine Learning integration in Stream Analytics
 Stream Analytics supports user-defined functions that call out to Azure Machine Learning endpoints. REST API support for this feature is detailed in the [Stream Analytics REST API library](https://msdn.microsoft.com/library/azure/dn835031.aspx). This article provides supplemental information needed for successful implementation of this capability in Stream Analytics. A tutorial has also been posted and is available [here](stream-analytics-machine-learning-integration-tutorial.md).
@@ -45,7 +44,7 @@ As an example, the following sample code creates a scalar UDF named *newudf* tha
 
 Example request body:  
 
-````
+```json
     {
         "name": "newudf",
         "properties": {
@@ -61,7 +60,7 @@ Example request body:
             }
         }
     }
-````
+```
 
 ## Call RetrieveDefaultDefinition endpoint for default UDF
 Once the skeleton UDF is created the complete definition of the UDF is needed. The RetreiveDefaultDefinition endpoint helps you get the default definition for a scalar function that is bound to an Azure Machine Learning endpoint. The payload below requires you to get the default UDF definition for a scalar function that is bound to an Azure Machine Learning endpoint. It doesn’t specify the actual endpoint as it has already been provided during PUT request. Stream Analytics calls the endpoint provided in the request if it is provided explicitly. Otherwise it uses the one originally referenced. Here the UDF takes a single string parameter (a sentence) and returns a single output of type string which indicates the "sentiment" label for that sentence.
@@ -72,19 +71,19 @@ POST : /subscriptions/<subscriptionId>/resourceGroups/<resourceGroup>/providers/
 
 Example request body:  
 
-````
+```json
     {
         "bindingType": "Microsoft.MachineLearning/WebService",
         "bindingRetrievalProperties": {
             "executeEndpoint": null,
             "udfType": "Scalar"
         }
     }
-````
+```
 
 A sample output of this would look something like below.  
 
-````
+```json
     {
         "name": "newudf",
         "properties": {
@@ -120,7 +119,7 @@ A sample output of this would look something like below.
             }
         }
     }
-````
+```
 
 ## Patch UDF with the response
 Now the UDF must be patched with the previous response, as shown below.
@@ -131,7 +130,7 @@ PATCH : /subscriptions/<subscriptionId>/resourceGroups/<resourceGroup>/providers
 
 Request Body (Output from RetrieveDefaultDefinition):
 
-````
+```json
     {
         "name": "newudf",
         "properties": {
@@ -167,20 +166,20 @@ Request Body (Output from RetrieveDefaultDefinition):
             }
         }
     }
-````
+```
 
 ## Implement Stream Analytics transformation to call the UDF
 Now query the UDF (here named scoreTweet) for every input event and write a response for that event to an output.  
 
-````
+```json
     {
         "name": "transformation",
         "properties": {
             "streamingUnits": null,
             "query": "select *,scoreTweet(Tweet) TweetSentiment into blobOutput from blobInput"
         }
     }
-````
+```
 
 
 ## Get help

articles/stream-analytics/media/stream-analytics-high-frequency-trading/trades.png renamed to articles/stream-analytics/media/stream-analytics-high-frequency-trading/trades-power-bi-chart.png

@@ -4,18 +4,18 @@ description: Learn about Stream Analytics, a managed service that helps you anal
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: overview
 ms.workload: data-services
 ms.custom: mvc
-ms.date: 03/27/2018
+ms.date: 12/07/2018
+ms.custom: seodec18
 #Customer intent: "What is Azure Stream Analytics and why should I care? As a IT Pro or developer, how do I use Stream Analytics to perform analytics on data streams?".
 
 ---
 
-# What is Stream Analytics?
+# What is Azure Stream Analytics?
 
 Azure Stream Analytics is an event-processing engine that allows you to examine high volumes of data streaming from devices. Incoming data can be from devices, sensors, web sites, social media feeds, applications, and more. It also supports extracting information from data streams, identifying patterns, and relationships. You can then use these patterns to trigger other actions downstream, like alerts, feed information to a reporting tool, or store it for later use.
 
@@ -39,13 +39,13 @@ After analyzing the incoming data, you specify an output for the transformed dat
 
 The following image illustrates the Stream Analytics pipeline, Your Stream Analytics job can use all or a selected set of inputs and outputs. This image shows how data is sent to Stream Analytics, analyzed, and sent for other actions like storage, or presentation:
 
-![Stream Analytics pipeline](./media/stream-analytics-introduction/stream_analytics_intro_pipeline.png)
+![Stream Analytics intro pipeline](./media/stream-analytics-introduction/stream-analytics-intro-pipeline.png)
 
 ## Key capabilities and benefits
 
 Azure Stream Analytics is designed to be easy to use, flexible, reliable, and scalable to any job size. It is available across multiple datacenters as well as sovereign clouds. Following image illustrates the key capabilities of Azure Stream Analytics:
 
-![Stream Analytics key capabilities](./media/stream-analytics-introduction/stream_analytics_key_capabilities.png)
+![Stream Analytics key capabilities](./media/stream-analytics-introduction/stream-analytics-key-capabilities.png)
 
 ## Ease of getting started
 

articles/stream-analytics/media/stream-analytics-high-frequency-trading/voi-formula.png renamed to articles/stream-analytics/media/stream-analytics-high-frequency-trading/volume-order-imbalance-formula.png

@@ -57,7 +57,7 @@ To create a simple JavaScript user-defined function under an existing Stream Ana
 4.	On the **New Function** blade, for **Function Type**, select **JavaScript**. A default function template appears in the editor.
 5.	For the **UDF alias**, enter **hex2Int**, and change the function implementation as follows:
 
-    ```
+    ```javascript
     // Convert Hex value to integer.
     function hex2Int(hexValue) {
         return parseInt(hexValue, 16);
@@ -72,7 +72,7 @@ To create a simple JavaScript user-defined function under an existing Stream Ana
 1. In the query editor, under **JOB TOPOLOGY**, select **Query**.
 2.	Edit your query, and then call the user-defined function, like this:
 
-    ```
+    ```SQL
     SELECT
         time,
         UDF.hex2Int(offset) AS IntOffset
@@ -128,14 +128,14 @@ If you have a follow-up processing step that uses a Stream Analytics job output
 
 **JavaScript user-defined function definition:**
 
-```
+```javascript
 function main(x) {
 return JSON.stringify(x);
 }
 ```
 
 **Sample query:**
-```
+```SQL
 SELECT
     DataString,
     DataValue,

articles/stream-analytics/media/stream-analytics-introduction/stream_analytics_intro_pipeline.png renamed to articles/stream-analytics/media/stream-analytics-introduction/stream-analytics-intro-pipeline.png

@@ -4,11 +4,11 @@ description: This article describes how to analyze diagnostics logs in Azure Str
 services: stream-analytics
 author: jseb225
 ms.author: jeanb
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 04/20/2017
+ms.date: 12/07/2018
+ms.custom: seodec18
 ---
 # Troubleshoot Azure Stream Analytics by using diagnostics logs
 
@@ -30,23 +30,23 @@ Diagnostics logs are **off** by default. To turn on diagnostics logs, complete t
 
 1.  Sign in to the Azure portal, and go to the streaming job blade. Under **Monitoring**, select **Diagnostics logs**.
 
-    ![Blade navigation to diagnostics logs](./media/stream-analytics-job-diagnostic-logs/image1.png)  
+    ![Blade navigation to diagnostics logs](./media/stream-analytics-job-diagnostic-logs/diagnostic-logs-monitoring.png)  
 
 2.  Select **Turn on diagnostics**.
 
-    ![Turn on diagnostics logs](./media/stream-analytics-job-diagnostic-logs/image2.png)
+    ![Turn on Stream Analytics diagnostics logs](./media/stream-analytics-job-diagnostic-logs/turn-on-diagnostic-logs.png)
 
 3.  On the **Diagnostics settings** page, for **Status**, select **On**.
 
-    ![Change status for diagnostics logs](./media/stream-analytics-job-diagnostic-logs/image3.png)
+    ![Change status for diagnostics logs](./media/stream-analytics-job-diagnostic-logs/save-diagnostic-log-settings.png)
 
 4.  Set up the archival target (storage account, event hub, Log Analytics) that you want. Then, select the categories of logs that you want to collect (Execution, Authoring). 
 
 5.  Save the new diagnostics configuration.
 
 The diagnostics configuration takes about 10 minutes to take effect. After that, the logs start appearing in the configured archival target (you can see these on the **Diagnostics logs** page):
 
-![Blade navigation to diagnostics logs - archival targets](./media/stream-analytics-job-diagnostic-logs/image4.png)
+![Blade navigation to diagnostics logs - archival targets](./media/stream-analytics-job-diagnostic-logs/view-diagnostic-logs-page.png)
 
 For more information about configuring diagnostics, see [Collect and consume diagnostics data from your Azure resources](https://docs.microsoft.com/azure/monitoring-and-diagnostics/monitoring-overview-of-diagnostic-logs).
 

articles/stream-analytics/media/stream-analytics-introduction/stream_analytics_key_capabilities.png renamed to articles/stream-analytics/media/stream-analytics-introduction/stream-analytics-key-capabilities.png

@@ -3,12 +3,12 @@ title: Avoid service interruptions in Azure Stream Analytics jobs
 description: This article describes guidance on making your Stream Analytics jobs upgrade resilient.
 services: stream-analytics
 author: jseb225
-manager: kfile
 ms.author: jeanb
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 03/28/2017
+ms.date: 12/07/2018
+ms.custom: seodec18
 ---
 
 # Guarantee Stream Analytics job reliability during service updates
@@ -24,3 +24,11 @@ _With the exception of Central India_ (whose paired region, South India, does no
 The article on **[availability and paired regions](https://docs.microsoft.com/azure/best-practices-availability-paired-regions)** has the most up-to-date information on which regions are paired.
 
 Customers are advised to deploy identical jobs to both paired regions. In addition to Stream Analytics internal monitoring capabilities, customers are also advised to monitor the jobs as if **both** are production jobs. If a break is identified to be a result of the Stream Analytics service update, escalate appropriately and fail over any downstream consumers to the healthy job output. Escalation to support will prevent the paired region from being affected by the new deployment and maintain the integrity of the paired jobs.
+
+## Next steps
+
+* [Introduction to Stream Analytics](stream-analytics-introduction.md)
+* [Get started with Stream Analytics](stream-analytics-real-time-fraud-detection.md)
+* [Scale Stream Analytics jobs](stream-analytics-scale-jobs.md)
+* [Stream Analytics query language reference](https://msdn.microsoft.com/library/azure/dn834998.aspx)
+* [Stream Analytics management REST API reference](https://msdn.microsoft.com/library/azure/dn835031.aspx)

articles/stream-analytics/media/stream-analytics-job-diagnostic-logs/image1.png renamed to articles/stream-analytics/media/stream-analytics-job-diagnostic-logs/diagnostic-logs-monitoring.png

@@ -1,13 +1,14 @@
 ---
-title: Test live data locally using Azure Stream Analytics tools for Visual Studio (Preview)
+title: Test live data with Azure Stream Analytics for Visual Studio
 description: Learn how to test your Azure Stream Analytics job locally using live streaming data.
 services: stream-analytics
 author: mamccrea
 ms.author: mamccrea
 ms.reviewer: mamccrea
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 09/24/2018
+ms.date: 12/07/2018
+ms.custom: seodec18
 ---
 
 # Test live data locally using Azure Stream Analytics tools for Visual Studio (Preview)
@@ -28,28 +29,28 @@ The following local testing options are supported:
 
 1. After you've created an [Azure Stream Analytics cloud project in Visual Studio](stream-analytics-quick-create-vs.md), open **script.asaql**. The local testing uses local input and local output by default.
 
-   ![Azure Stream Analytics Visual Studio local testing with local input and local output](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-local-input-output.png)
+   ![Azure Stream Analytics Visual Studio local input and local output](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-local-input-output.png)
 
 2. To test live data, choose **Use Cloud Input** from the dropdown box.
 
-   ![Azure Stream Analytics Visual Studio local testing with live cloud input](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input.png)
+   ![Azure Stream Analytics Visual Studio live cloud input](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input.png)
 
 
 3. Set the **Start Time** to define when the job will start processing input data. The job might need to read input data ahead of time to ensure accurate results. The default time is set to 30 minutes ahead of the current time.
 
-   ![Azure Stream Analytics Visual Studio local testing with live data start time](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input-start-time.png)
+   ![Azure Stream Analytics Visual Studio live data start time](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input-start-time.png)
 
 4. Click **Run Locally**. A console window will appear with the running progress and job metrics. If you want to stop the process, you can do so manually. 
 
-   ![Azure Stream Analytics Visual Studio local testing with live data process window](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input-process-window.png)
+   ![Azure Stream Analytics Visual Studio live data process window](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input-process-window.png)
 
    The output results are refreshed every three seconds with the first 500 output rows in the local run result window, and the output files are placed in your project path **ASALocalRun** folder. You can also open the output files by clicking **Open Results Folder** button in the local run result window.
 
-   ![Azure Stream Analytics Visual Studio local testing with live data open results folder](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input-open-results-folder.png)
+   ![Azure Stream Analytics Visual Studio live data open results folder](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-input-open-results-folder.png)
 
 5. If you want to output the results to your cloud output sinks, choose **Output to Cloud** from the second dropdown box. Power BI and Azure Data Lake Storage are not supported output sinks.
 
-   ![Azure Stream Analytics Visual Studio local testing with live data output to cloud](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-output.png)
+   ![Azure Stream Analytics Visual Studio live data output to cloud](./media/stream-analytics-live-data-local-testing/stream-analytics-local-testing-cloud-output.png)
 
 ## Limitations
 

articles/stream-analytics/media/stream-analytics-job-diagnostic-logs/image3.png renamed to articles/stream-analytics/media/stream-analytics-job-diagnostic-logs/save-diagnostic-log-settings.png

@@ -2,13 +2,13 @@
 title: Rotate login credentials in Azure Stream Analytics jobs
 description: This article describes how to update the credentials of inputs and output sinks in Azure Stream Analytics jobs.
 services: stream-analytics
-author: jasonwhowell
+author: mamccrea
 ms.author: mamccrea
-manager: kfile
 ms.reviewer: jasonh
 ms.service: stream-analytics
 ms.topic: conceptual
-ms.date: 01/11/2018
+ms.date: 12/07/2018
+ms.custom: seodec18
 ---
 # Rotate login credentials for inputs and outputs of a Stream Analytics Job
 
@@ -21,20 +21,20 @@ In this section, we will walk you through regenerating credentials for Blob Stor
 ### Blob storage/Table storage
 1. Sign in to the Azure portal > browse the storage account that you used as input/output for the Stream Analytics job.    
 2. From the settings section, open **Access keys**. Between the two default keys (key1, key2), pick the one that is not used by your job and regenerate it:  
-   ![Regenerate keys for storage account](media/stream-analytics-login-credentials-inputs-outputs/image1.png)
+   ![Regenerate keys for storage account](media/stream-analytics-login-credentials-inputs-outputs/regenerate-storage-keys.png)
 3. Copy the newly generated key.    
 4. From the Azure portal, browse your Stream Analytics job > select **Stop** and wait for the job to stop.    
 5. Locate the Blob/Table storage input/output for which you want to update credentials.    
 6. Find the **Storage Account Key** field and paste your newly generated key > click **Save**.    
 7. A connection test will automatically start when you save your changes, you can view it from the notifications tab. There are two notifications- one corresponds to saving the update and other corresponds to testing the connection:  
-   ![Notifications after editing the key](media/stream-analytics-login-credentials-inputs-outputs/image4.png)
+   ![Notifications after editing the key](media/stream-analytics-login-credentials-inputs-outputs/edited-key-notifications.png)
 8. Proceed to [start your job from the last stopped time](#start-your-job-from-the-last-stopped-time) section.
 
 ### Event hubs
 
 1. Sign in to the Azure portal > browse the Event Hub that you used as input/output for the Stream Analytics job.    
 2. From the settings section, open **Shared access policies** and select the required access policy. Between the **Primary Key** and **Secondary Key**, pick the one that is not used by your job and regenerate it:  
-   ![Regenerate keys for Event Hub](media/stream-analytics-login-credentials-inputs-outputs/image2.png)
+   ![Regenerate keys for Event Hubs](media/stream-analytics-login-credentials-inputs-outputs/regenerate-event-hub-keys.png)
 3. Copy the newly generated key.    
 4. From the Azure portal, browse your Stream Analytics job > select **Stop** and wait for the job to stop.    
 5. Locate the Event hubs input/output for which you want to update credentials.    
@@ -48,7 +48,7 @@ You need to connect to the SQL database to update the login credentials of an ex
 
 1. Sign in to the Azure portal > browse the SQL database that you used as output for the Stream Analytics job.    
 2. From **Data explorer**, login/connect to your database > select Authorization type as **SQL server authentication** > type in your **Login** and **Password** details > Select **Ok**.  
-   ![Regenerate credentials for SQL database](media/stream-analytics-login-credentials-inputs-outputs/image3.png)
+   ![Regenerate credentials for SQL database](media/stream-analytics-login-credentials-inputs-outputs/regenerate-sql-credentials.png)
 
 3. In the query tab, alter the password for one of your user's by running the following query (make sure to replace `<user_name>` with your username and `<new_password>` with your new password):  
 
@@ -73,7 +73,7 @@ You need to connect to the SQL database to update the login credentials of an ex
 
 1. Navigate to the job's **Overview** pane > select **Start** to start the job.    
 2. Select **When last stopped** > click **Start**. Note that the "When last stopped" option only appears if you previously ran the job and had some output generated. The job is restarted based on the last output value's time.
-   ![Start the job](media/stream-analytics-login-credentials-inputs-outputs/image5.png)
+   ![Start the Stream Analytics job](media/stream-analytics-login-credentials-inputs-outputs/start-stream-analytics-job.png)
 
 ## Next steps
 * [Introduction to Azure Stream Analytics](stream-analytics-introduction.md)