Merge pull request #31897 from WilliamDAssafMSFT/20240920-image-cleanup

[SCOPED] 20240920 SFI image pass

Author: JamesJBarnett

azure-sql/database/media/saas-multitenantdb-tenant-analytics/ticketextracts.png

@@ -15,18 +15,18 @@ ms.custom: sqldbrb=1
 
 In this tutorial, you walk through a complete analytics scenario for a multitenant implementation. The scenario demonstrates how analytics can enable businesses to make smart decisions. Using data extracted from sharded database, you use analytics to gain insights into tenant behavior, including their use of the sample Wingtip Tickets SaaS application. This scenario involves three steps: 
 
-1.	**Extract data** from each tenant database into an analytics store.
-2.	**Optimize the extracted data** for analytics processing.
-3.	Use **Business Intelligence** tools to draw out useful insights, which can guide decision making. 
+1.    **Extract data** from each tenant database into an analytics store.
+2.    **Optimize the extracted data** for analytics processing.
+3.    Use **Business Intelligence** tools to draw out useful insights, which can guide decision making. 
 
 In this tutorial you learn how to:
 
 > [!div class="checklist"]
 > - Create the tenant analytics store to extract the data into.
 > - Use elastic jobs to extract data from each tenant database into the analytics store.
 > - Optimize the extracted data (reorganize into a star-schema).
-> -	Query the analytics database.
-> -	Use Power BI for data visualization to highlight trends in tenant data and make recommendation for improvements.
+> -    Query the analytics database.
+> -    Use Power BI for data visualization to highlight trends in tenant data and make recommendation for improvements.
 
 ![Diagram shows an overview of the architecture used for this article.](./media/saas-multitenantdb-tenant-analytics/architectureOverview.png)
 
@@ -131,8 +131,6 @@ Each job extracts its data, and posts it into the analytics store. There a separ
 4. Press **F5** to run the script that creates and runs the job that extracts tickets and customers data from each tenant database. The job saves the data into the analytics store.
 5. Query the TicketsRawData table in the tenantanalytics database, to ensure that the table is populated with tickets information from all tenants.
 
-![Screenshot shows the ExtractTickets database with the TicketsRawData d b o selected in Object Explorer.](./media/saas-multitenantdb-tenant-analytics/ticketExtracts.png)
-
 Repeat the preceding steps, except this time replace **\ExtractTickets.sql** with **\ExtractVenuesEvents.sql** in step 2.
 
 Successfully running the job populates the EventsRawData table in the analytics store with new events and venues information from all tenants. 
@@ -226,8 +224,8 @@ In this tutorial, you learned how to:
 > - Deployed a tenant analytics database with pre-defined star schema tables
 > - Used elastic jobs to extract data from all the tenant database
 > - Merge the extracted data into tables in a star-schema designed for analytics
-> -	Query an analytics database 
-> -	Use Power BI for data visualization to observe trends in tenant data 
+> -    Query an analytics database 
+> -    Use Power BI for data visualization to observe trends in tenant data 
 
 Congratulations!
 

azure-sql/database/media/saas-tenancy-tenant-analytics/ticketextracts.png

@@ -15,18 +15,18 @@ ms.custom: sqldbrb=1
 
 In this tutorial, you walk through a complete analytics scenario for a single tenant implementation. The scenario demonstrates how analytics can enable businesses to make smart decisions. Using data extracted from each tenant database, you use analytics to gain insights into tenant behavior, including their use of the sample Wingtip Tickets SaaS application. This scenario involves three steps: 
 
-1.	**Extract** data from each tenant database and **Load** into an analytics store.
-2.	**Transform the extracted data** for analytics processing.
-3.	Use **business intelligence** tools to draw out useful insights, which can guide decision making. 
+1.    **Extract** data from each tenant database and **Load** into an analytics store.
+2.    **Transform the extracted data** for analytics processing.
+3.    Use **business intelligence** tools to draw out useful insights, which can guide decision making. 
 
 In this tutorial you learn how to:
 
 > [!div class="checklist"]
 > - Create the tenant analytics store to extract the data into.
 > - Use elastic jobs to extract data from each tenant database into the analytics store.
 > - Optimize the extracted data (reorganize into a star-schema).
-> -	Query the analytics database.
-> -	Use Power BI for data visualization to highlight trends in tenant data and make recommendation for improvements.
+> -    Query the analytics database.
+> -    Use Power BI for data visualization to highlight trends in tenant data and make recommendation for improvements.
 
 ![Diagram shows an overview of the architecture used for this article.](./media/saas-tenancy-tenant-analytics/architectureOverview.png)
 
@@ -130,8 +130,6 @@ Each job extracts its data, and posts it into the analytics store. There a separ
 4. Press F5 to run the script that creates and runs the job that extracts tickets and customers data from each tenant database. The job saves the data into the analytics store.
 5. Query the TicketsRawData table in the tenantanalytics database, to ensure that the table is populated with tickets information from all tenants.
 
-![Screenshot shows the ExtractTickets database with the TicketsRawData d b o selected in Object Explorer.](./media/saas-tenancy-tenant-analytics/ticketExtracts.png)
-
 Repeat the preceding steps, except this time replace **\ExtractTickets.sql** with **\ExtractVenuesEvents.sql** in step 2.
 
 Successfully running the job populates the EventsRawData table in the analytics store with new events and venues information from all tenants. 
@@ -225,8 +223,8 @@ In this tutorial, you learned how to:
 > - Deployed a tenant analytics database with pre-defined star schema tables
 > - Used elastic jobs to extract data from all the tenant database
 > - Merge the extracted data into tables in a star-schema designed for analytics
-> -	Query an analytics database 
-> -	Use Power BI for data visualization to observe trends in tenant data 
+> -    Query an analytics database 
+> -    Use Power BI for data visualization to observe trends in tenant data 
 
 Congratulations!
 

azure-sql/database/saas-multitenantdb-tenant-analytics.md

@@ -86,8 +86,6 @@ Note the IP address (`10.1.1.3` in this example) and the port number (`30080`) i
 
 One of the other ways to get this information is doing right-click Manage on the server in Azure Data Studio where you'll find the endpoints of the services listed.
 
-![ADS End Point](media/big-data-cluster-consume-apps/ads_end_point.png)
-
 ## Generate a JWT access token
 
 To access the RESTful web service for the app you've deployed you first have to generate a JWT Access token. The URL for the access token depends on the version of Big Data Cluster. 

azure-sql/database/saas-tenancy-tenant-analytics.md

@@ -80,8 +80,6 @@ azdata bdc endpoint list
 
 Another way to retrieve this information is to navigate to the server in *Azure Data Studio* and right-click **Manage**. The endpoints for services are listed.
 
-![ADS End Point](media/vs-extension/ads_end_point.png)
-
 Find the endpoint you want to use, then connect to the cluster.
 
 ![New Connection](media/vs-extension/connect_to_cluster.png)