Merge pull request #102542 from XiaoyuMSFT/rcsiupdate

rcsi updates

Author: PRMerger7

articles/sql-data-warehouse/sql-data-warehouse-develop-transactions.md

@@ -20,7 +20,7 @@ Tips for implementing transactions in Azure SQL Data Warehouse for developing so
 As you would expect, SQL Data Warehouse supports transactions as part of the data warehouse workload. However, to ensure the performance of SQL Data Warehouse is maintained at scale some features are limited when compared to SQL Server. This article highlights the differences and lists the others. 
 
 ## Transaction isolation levels
-SQL Data Warehouse implements ACID transactions. However, the isolation level of the transactional support is limited to READ UNCOMMITTED; this level cannot be changed. If READ UNCOMMITTED is a concern, you can implement a number of coding methods to prevent dirty reads of data. The most popular methods use both CTAS and table partition switching (often known as the sliding window pattern) to prevent users from querying data that is still being prepared. Views that pre-filter the data are also a popular approach.  
+SQL Data Warehouse implements ACID transactions. The isolation level of the transactional support is default to READ UNCOMMITTED.  You can change it to READ COMMITTED SNAPSHOT ISOLATION by turning ON the READ_COMMITTED_SNAPSHOT database option for a user database when connected to the master database.  Once enabled, all transactions in this database are executed under READ COMMITTED SNAPSHOT ISOLATION and setting READ UNCOMMITTED on session level will not be honored. Check [ALTER DATABASE SET options (Transact-SQL)](https://docs.microsoft.com/sql/t-sql/statements/alter-database-transact-sql-set-options?view=azure-sqldw-latest) for details.
 
 ## Transaction size
 A single data modification transaction is limited in size. The limit is applied per distribution. Therefore, the total allocation can be calculated by multiplying the limit by the distribution count. To approximate the maximum number of rows in the transaction divide the distribution cap by the total size of each row. For variable length columns, consider taking an average column length rather than using the maximum size.