park rename

Author: filippopovic

articles/synapse-analytics/sql/best-practices-sql-on-demand.md

@@ -114,9 +114,9 @@ For more information, check [filename](develop-storage-files-overview.md#filenam
 > Always cast result of filepath and fileinfo functions to appropriate data types. If you use character data types, make sure appropriate length is used.
 
 > [!NOTE]
-> Functions used for partition elimination, filepath and fileinfo, are not currently supported for external tables other than those created automatically for each table created in Synapse Spark.
+> Functions used for partition elimination, filepath and fileinfo, are not currently supported for external tables other than those created automatically for each external table created in Apache Spark for Azure Synapse.
 
-If your stored data isn't partitioned, consider partitioning it so you can use these functions to optimize queries targeting those files. When [querying partitioned Spark tables](develop-storage-files-spark-tables.md) from SQL on-demand, the query will automatically target only the files needed.
+If your stored data isn't partitioned, consider partitioning it so you can use these functions to optimize queries targeting those files. When [querying partitioned Apache Spark for Azure Synapse tables](develop-storage-files-spark-tables.md) from SQL on-demand, the query will automatically target only the files needed.
 
 ## Use PARSER_VERSION 2.0 for querying CSV files
 

articles/synapse-analytics/sql/develop-best-practices.md

@@ -149,7 +149,7 @@ Consequently, you will achieve better performance. For more information, check [
 
 If your data in storage is not partitioned, consider partitioning it so you can use these functions to optimize queries targeting those files.
 
-When [querying partitioned Spark tables](develop-storage-files-spark-tables.md) from SQL on-demand, the query will automatically target only files needed.
+When [querying partitioned Apache Spark for Azure Synapse external tables](develop-storage-files-spark-tables.md) from SQL on-demand, the query will automatically target only files needed.
 
 ### Use CETAS to enhance query performance and joins
 

articles/synapse-analytics/sql/develop-storage-files-spark-tables.md

@@ -1,5 +1,5 @@
 ---
-title: Query Spark tables using SQL on-demand (preview)
+title: Synchronize Apache Spark for Azure Synapse external table definitions in SQL on-demand (preview)
 description: Overview of how to query Spark tables using SQL on-demand (preview)
 services: synapse-analytics 
 author: julieMSFT
@@ -11,9 +11,9 @@ ms.author: jrasnick
 ms.reviewer: jrasnick
 ---
 
-# Query Spark tables with Azure Synapse Analytics using SQL on-demand (preview)
+# Synchronize Apache Spark for Azure Synapse external table definitions in SQL on-demand (preview)
 
-The SQL on-demand (preview) can automatically synchronize metadata from Spark pools within Synapse workspace (preview). A SQL on-demand database will be created for each database existing in Spark pools (preview). 
+The SQL on-demand (preview) can automatically synchronize metadata from Apache Spark for Azure Synapse pools. A SQL on-demand database will be created for each database existing in Spark pools (preview). 
 
 For each Spark external table based on Parquet and located in Azure Storage, an external table is created in the SQL on-demand database. As such, you can shut down your Spark pools and still query Spark external tables from SQL on-demand.
 

articles/synapse-analytics/sql/develop-tables-cetas.md

@@ -168,4 +168,4 @@ The following data types cannot be used in SELECT part of CETAS:
 
 ## Next steps
 
-You can try querying [Spark tables](develop-storage-files-spark-tables.md).
+You can try querying [Apache Spark for Azure Synapse external tables](develop-storage-files-spark-tables.md).

articles/synapse-analytics/sql/develop-tables-external-tables.md

@@ -376,4 +376,4 @@ The external table is now created, for future exploration of the content of this
 
 ## Next steps
 
-Check the [CETAS](develop-tables-cetas.md) article for how to save the query results to an external table in Azure Storage. Or you can start querying [Spark tables](develop-storage-files-spark-tables.md).
+Check the [CETAS](develop-tables-cetas.md) article for how to save the query results to an external table in Azure Storage. Or you can start querying [Apache Spark for Azure Synapse external tables](develop-storage-files-spark-tables.md).

articles/synapse-analytics/sql/on-demand-workspace-overview.md

@@ -22,7 +22,7 @@ SQL on-demand is a distributed data processing system, built for large scale of
 
 SQL on-demand is serverless, hence there is no infrastructure to setup or clusters to maintain. A default endpoint for this service is provided within every Azure Synapse workspace, so you can start querying data as soon as the workspace is created. There is no charge for resources reserved, you are only being charged for the data scanned by queries you run, hence this model is a true pay-per-use model.  
 
-If you use Spark in your data pipeline, for data preparation, cleansing or enrichment, you can [query any Spark tables](develop-storage-files-spark-tables.md) you've created in the process, directly from SQL on-demand. Use [Private Link](../security/how-to-connect-to-workspace-with-private-links.md) to bring your SQL on-demand endpoint into your [managed workspace VNet](../security/synapse-workspace-managed-vnet.md).  
+If you use Apache Spark for Azure Synapse in your data pipeline, for data preparation, cleansing or enrichment, you can [query external Spark tables](develop-storage-files-spark-tables.md) you've created in the process, directly from SQL on-demand. Use [Private Link](../security/how-to-connect-to-workspace-with-private-links.md) to bring your SQL on-demand endpoint into your [managed workspace VNet](../security/synapse-workspace-managed-vnet.md).  
 
 ## Who is SQL on-demand for
 
@@ -36,7 +36,7 @@ Different professional roles can benefit from SQL on-demand:
 
 - Data Engineers can explore the lake, transform and prepare data using this service, and simplify their data transformation pipelines. For more information, check this [tutorial](tutorial-data-analyst.md).
 - Data Scientists can quickly reason about the contents and structure of the data in the lake, thanks to features such as OPENROWSET and automatic schema inference.
-- Data Analysts can [explore data and Spark tables](develop-storage-files-spark-tables.md) created by Data Scientists or Data Engineers using familiar T-SQL language or their favorite tools, which can connect to SQL on-demand.
+- Data Analysts can [explore data and Spark external tables](develop-storage-files-spark-tables.md) created by Data Scientists or Data Engineers using familiar T-SQL language or their favorite tools, which can connect to SQL on-demand.
 - BI Professionals can quickly [create Power BI reports on top of data in the lake](tutorial-connect-power-bi-desktop.md) and Spark tables.
 
 ## What do I need to do to start using it?

articles/synapse-analytics/toc.yml

@@ -316,7 +316,7 @@
           href: sql/develop-storage-files-overview.md
         - name: Storage Access Control
           href: sql/develop-storage-files-storage-access-control.md
-        - name: Spark tables
+        - name: Apache Spark for Azure Synapse external tables
           href: sql/develop-storage-files-spark-tables.md
     - name: Workload management
       items: