Merge pull request #111489 from julieMSFT/20200414_publishCHANGES

20200414 publish changes

Author: garycentric

articles/synapse-analytics/metadata/database.md

@@ -29,8 +29,6 @@ Since the databases are synchronized to SQL on-demand and the SQL pools asynchro
 
 ## Manage a Spark created database
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 Use Spark to manage Spark created databases. For example, delete it through a Spark pool job, and create tables in it from Spark.
 
 If you create objects in a Spark created database using SQL on-demand, or try to drop the database, the operation will succeed. But, the original Spark database won't be changed.
@@ -39,8 +37,6 @@ If you try to drop the synchronized schema in a SQL pool, or try to create a tab
 
 ## Handling of name conflicts
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 If the name of a Spark database conflicts with the name of an existing SQL on-demand database, a suffix is appended in SQL on-demand to the Spark database. The suffix in SQL on-demand is `_<workspace name>-ondemand-DefaultSparkConnector`.
 
 For example, if a Spark database called `mydb` gets created in the Azure Synapse workspace `myws` and a SQL on-demand database with that name already exists, then the Spark database in SQL on-demand will have to be referenced using the name `mydb_myws-ondemand-DefaultSparkConnector`.
@@ -50,8 +46,6 @@ For example, if a Spark database called `mydb` gets created in the Azure Synapse
 
 ## Security model
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 The Spark databases and tables, along with their synchronized representations in the SQL engines will be secured at the underlying storage level.
 
 The security principal who creates a database is considered the owner of that database, and has all the rights to the database and its objects.
@@ -66,8 +60,6 @@ If a security principal requires the ability to create objects or drop objects i
 
 ### Create & connect to Spark database - SQL on-demand
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 First create a new Spark database named `mytestdb` using a Spark cluster you have already created in your workspace. You can achieve that, for example,  using a Spark C# Notebook with the following .NET for Spark statement:
 
 ```csharp
@@ -84,8 +76,6 @@ Verify that `mytestdb` is included in the results.
 
 ### Exposing a Spark database in a SQL pool
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 With the database created in the previous example, now create a SQL pool in your workspace named `mysqlpool` that enables metadata synchronization.
 
 Run the following statement against the `mysqlpool` SQL pool:

articles/synapse-analytics/metadata/overview.md

@@ -41,14 +41,10 @@ Object synchronization occurs asynchronously. Objects will have a slight delay o
 
 ## Which metadata objects are shared
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 Spark allows you to create databases, external tables, managed tables, and views. Since Spark views require a Spark engine to process the defining Spark SQL statement, and cannot be processed by a SQL engine, only databases and their contained external and managed tables that use the Parquet storage format are shared with the workspace SQL engines. Spark views are only shared among the Spark pool instances.
 
 ## Security model at a glance
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
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 The Spark databases and tables, along with their synchronized representations in the SQL engines, are secured at the underlying storage level. When the table is queried by any of the engines that the query submitter has the right to use, the query submitter's security principal is being passed through to the underlying files. Permissions are checked at the file system level.
 
 For more information, see [Azure Synapse Analytics shared database](database.md).

articles/synapse-analytics/metadata/table.md

@@ -27,8 +27,6 @@ Mapping of tables to external tables, data sources and file formats.
 
 ## Manage a Spark created table
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 Use Spark to manage Spark created databases. For example, delete it through a Spark pool job, and create tables in it from Spark.
 
 If you create objects in such a database from SQL on-demand or try to drop the database, the operation will succeed, but the original Spark database will not be changed.
@@ -37,8 +35,6 @@ If you try to drop the synchronized schema in a SQL pool, or try to create a tab
 
 ## Exposing a Spark table in SQL
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 ### Which Spark tables are shared
 
 Spark provides two types of tables that Azure Synapse exposes in SQL automatically:
@@ -87,8 +83,6 @@ Spark tables provide different data types than the Synapse SQL engines. The foll
 
 ## Security model
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 The Spark databases and tables, as well as their synchronized representations in the SQL engines will be secured at the underlying storage level. Since they do not currently have permissions on the objects themselves, the objects can be seen in the object explorer.
 
 The security principal who creates a managed table is considered the owner of that table and has all the rights to the table as well as the underlying folders and files. In addition, the owner of the database will automatically become co-owner of the table.
@@ -101,8 +95,6 @@ For more information on how to set permissions on the folders and files, see [Az
 
 ### Create a managed table backed by Parquet in Spark and query from SQL on-demand
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 In this scenario, you have a Spark database named `mytestdb`. See [Create & connect to Spark database - SQL on-demand](database.md#create--connect-to-spark-database---sql-on-demand).
 
 Create a managed Spark table with SparkSQL by running the following command:
@@ -162,8 +154,6 @@ id | name | birthdate
 
 ### Creating an external table backed by Parquet in Spark and querying it from SQL on-demand
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 In this example, create an external Spark table over the Parquet data files that got created in the previous example for the managed table.
 
 For example, with SparkSQL run:
@@ -201,8 +191,6 @@ id | name | birthdate
 
 ### Querying Spark tables in a SQL pool
 
-[!INCLUDE [synapse-analytics-preview-features](../../../includes/synapse-analytics-preview-features.md)]
-
 With the tables created in the previous examples, now create a SQL pool in your workspace named `mysqlpool` that enables metadata synchronization (or use the already created pool from [Exposing a Spark database in a SQL pool](database.md#exposing-a-spark-database-in-a-sql-pool).
 
 Run the following statement against the `mysqlpool` SQL pool:

articles/synapse-analytics/sql-data-warehouse/media/sql-data-warehouse-troubleshoot-connectivity/diagnostics-link.png

@@ -17,15 +17,6 @@ ms.custom: seo-lt-2019, azure-synapse
 
 This article lists common troubleshooting techniques around connecting to your SQL Analytics database.
 
-- [Check service availability](sql-data-warehouse-troubleshoot-connectivity.md#check-service-availability)
-- [Check for paused or scaling operation](sql-data-warehouse-troubleshoot-connectivity.md#check-for-paused-or-scaling-operation)
-- [Check your firewall settings](sql-data-warehouse-troubleshoot-connectivity.md#check-your-firewall-settings)
-- [Check your VNet/Service Endpoint settings](sql-data-warehouse-troubleshoot-connectivity.md#check-your-vnetservice-endpoint-settings)
-- [Check for the latest drivers](sql-data-warehouse-troubleshoot-connectivity.md#check-for-the-latest-drivers)
-- [Check your connection string](sql-data-warehouse-troubleshoot-connectivity.md#check-your-connection-string)
-- [Intermittent connection issues](sql-data-warehouse-troubleshoot-connectivity.md#intermittent-connection-issues)
-- [Common error messages](sql-data-warehouse-troubleshoot-connectivity.md#common-error-messages)
-
 ## Check service availability
 
 Check to see if the service is available. In the Azure portal, go to the Synapse SQL pool you're trying to connect. In the left TOC panel, click on **Diagnose and solve problems**.

articles/synapse-analytics/sql-data-warehouse/media/sql-data-warehouse-troubleshoot-connectivity/overview-maintance-schedule.png

@@ -10,5 +10,7 @@ ms.author: jrasnick
 ms.custom: include file
 ---
 
->[!NOTE]
->This section describes preview feature functionality.
+> [!IMPORTANT]
+> Azure Synapse Analytics (workspaces) is currently in public preview.
+> This preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. 
+> For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).