Merge pull request #111134 from julieMSFT/20200413_ria_whatislinks

20200413 ria whatislinks

Author: PRMerger6

articles/synapse-analytics/overview-what-is.md

@@ -81,7 +81,8 @@ Azure Synapse provides a single way for enterprises to manage analytics resource
 
 ## Next steps
 
-* Explore [Azure Synapse architecture](https://review.docs.microsoft.com/azure/sql-data-warehouse/massively-parallel-processing-mpp-architecture)
-* Quickly [create a SQL pool](https://review.docs.microsoft.com/azure/synapse-analytics/sql-data-warehouse/create-data-warehouse-portal)
-* [Load sample data](https://review.docs.microsoft.com/azure/sql-data-warehouse/sql-data-warehouse-load-sample-databases)
-* Explore [Videos](https://azure.microsoft.com/documentation/videos/index/?services=sql-data-warehouse)
+* [Create a workspace](quickstart-create-workspace.md)
+* [Use Synapse Studio](quickstart-synapse-studio.md)
+* [Create a SQL pool](quickstart-create-sql-pool.md)
+* [Use SQL on-demand](quickstart-sql-on-demand.md)
+* [Create an Apache Spark pool](quickstart-create-apache-spark-pool.md)

articles/synapse-analytics/spark/synapse-spark-sql-pool-import-export.md

@@ -16,7 +16,7 @@ The Spark SQL Analytics Connector is designed to efficiently transfer data betwe
 
 ## Design
 
-Transferring data between Spark pools and SQL pools can be done using JDBC. However, given two distributed systems such as Spark and SQL pools (which provides massively parallel processing (MPP)), JDBC tends to be a bottleneck with serial data transfer.
+Transferring data between Spark pools and SQL pools can be done using JDBC. However, given two distributed systems such as Spark and SQL pools, JDBC tends to be a bottleneck with serial data transfer.
 
 The Spark pools to SQL Analytics Connector is a data source implementation for Apache Spark. It uses the Azure Data Lake Storage Gen 2, and Polybase in SQL pools to efficiently transfer data between the Spark cluster and the SQL Analytics instance.
 

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

@@ -50,8 +50,6 @@ SQL pool supports loading and exporting data through several tools including Azu
 > [!NOTE]
 > Polybase is the best choice when you are loading or exporting large volumes of data, or you need faster performance.
 
-PolyBase is designed to leverage the MPP (Massively Parallel Processing) architecture of SQL pool and will load and export data more quickly than any other tool.  
-
 PolyBase loads can be run using CTAS or INSERT INTO. CTAS will minimize transaction logging and is the fastest way to load your data. Azure Data Factory also supports PolyBase loads and can achieve performance similar to CTAS. PolyBase supports various file formats including Gzip files.
 
 To maximize throughput when using Gzip text files, break up files into 60 or more files to maximize parallelism of your load. For faster total throughput, consider loading data concurrently. Additional information for the topics relevant to this section is included in the following articles:

articles/synapse-analytics/sql/data-load-columnstore-compression.md

@@ -72,10 +72,6 @@ The trim_reason_desc tells whether the rowgroup was trimmed(trim_reason_desc = N
 
 ## How to estimate memory requirements
 
-<!--
-To view an estimate of the memory requirements to compress a rowgroup of maximum size into a columnstore index, download and run the view [dbo.vCS_mon_mem_grant](). This view shows the size of the memory grant that a rowgroup requires for compression in to the columnstore.
--->
-
 The maximum required memory to compress one rowgroup is approximately
 
 - 72 MB +
@@ -117,7 +113,7 @@ Another reason to avoid over-partitioning is there is a memory overhead for load
 
 The database shares the memory grant for a query among all the operators in the query. When a load query has complex sorts and joins, the memory available for compression is reduced.
 
-Design the load query to focus only on loading the query. If you need to run transformations on the data, run them separate from the load query. For example, stage the data in a heap table, run the transformations, and then load the staging table into the columnstore index. You can also load the data first and then use the MPP system to transform the data.
+Design the load query to focus only on loading the query. If you need to run transformations on the data, run them separate from the load query. For example, stage the data in a heap table, run the transformations, and then load the staging table into the columnstore index. 
 
 ### Adjust MAXDOP