Removed SQL Pool aspects

SQL Pool content will be re-added once they are ready (ETA: June/July 2020)

Author: MikeRys

articles/synapse-analytics/metadata/overview.md

@@ -6,19 +6,17 @@ author: MikeRys
 ms.service: synapse-analytics
 ms.topic: overview
 ms.subservice:
-ms.date: 04/15/2020
+ms.date: 05/01/2020
 ms.author: mrys
 ms.reviewer: jrasnick
 ---
 
 # Azure Synapse Analytics shared metadata
 
-Azure Synapse Analytics allows the different workspace computational engines to share databases and tables between its Spark pools (preview), SQL on-demand engine (preview), and SQL pools.
+Azure Synapse Analytics allows the different workspace computational engines to share databases and tables between its Spark pools (preview) and SQL on-demand engine (preview).
 
 [!INCLUDE [preview](../includes/note-preview.md)]
 
-
-
 The sharing supports the so-called modern data warehouse pattern and gives the workspace SQL engines access to databases and tables created with Spark. It also allows the SQL engines to create their own objects that aren't being shared with the other engines.
 
 ## Support the modern data warehouse
@@ -29,9 +27,7 @@ The shared metadata model supports the modern data warehouse pattern in the foll
 
 2. The Spark created databases and all their tables become visible in any of the Azure Synapse workspace Spark pool instances and can be used from any of the Spark jobs. This capability is subject to the [permissions](#security-model-at-a-glance) since all Spark pools in a workspace share the same underlying catalog meta store.
 
-3. The Spark created databases and their Parquet-backed tables become visible in the workspace SQL on-demand engine. [Databases](database.md) are created automatically in the SQL on-demand metadata, and both the [external and managed tables](table.md) created by a Spark job are made accessible as external tables in the SQL on-demand metadata in the `dbo` schema of the corresponding database. <!--For more details, see [ADD LINK].-->
-
-4. If there are SQL pool instances in the workspace that have their metadata synchronization enabled <!--[ADD LINK]--> or if a new SQL pool instance is created with the metadata synchronization enabled, the Spark created databases and their Parquet-backed tables will be mapped automatically into the SQL pool database as described in [Azure Synapse Analytics shared database](database.md).
+3. The Spark created databases and their Parquet-backed tables become visible in the workspace SQL on-demand engine. [Databases](database.md) are created automatically in the SQL on-demand metadata, and both the [external and managed tables](table.md) created by a Spark job are made accessible as external tables in the SQL on-demand metadata in the `dbo` schema of the corresponding database. 
 
 <!--[INSERT PICTURE]-->
 
@@ -41,17 +37,17 @@ Object synchronization occurs asynchronously. Objects will have a slight delay o
 
 ## Which metadata objects are shared
 
-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.
+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 engine. Spark views are only shared among the Spark pool instances.
 
 ## Security model at a glance
 
-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.
+The Spark databases and tables, along with their synchronized representations in the SQL engine, 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).
 
 ## Change maintenance
 
-If a metadata object is deleted or changed with Spark, the changes are picked up and propagated to the SQL on-demand engine and the SQL pools that have the objects synchronized. Synchronization is asynchronous and changes are reflected in the SQL engines after a short delay.
+If a metadata object is deleted or changed with Spark, the changes are picked up and propagated to the SQL on-demand engine. Synchronization is asynchronous and changes are reflected in the SQL engine after a short delay.
 
 ## Next steps