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Copy file name to clipboardExpand all lines: articles/synapse-analytics/sql-data-warehouse/release-notes-10-0-10106-0.md
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@@ -25,7 +25,7 @@ As new features are rolled out to all regions, check the version deployed to you
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Use the version identified to confirm which release has been applied to your SQL pool. The date in the output identifies the month for the release applied to your SQL pool.
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> [!NOTE]
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> By June 2020, the product name returned by SELECT @@VERSION will change from Microsoft Azure SQL Data Warehouse to Azure Synapse Analytics. We will publish the schedule in our release notes. This change is relevant for customers who parse product name from the result of SELECT @@VERSION in their application code. To avoid application code changes due to product rebranding, please use these commands to query SERVERPROPERTY for the database product name and version:
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> The product name returned by SELECT @@VERSION will change from Microsoft Azure SQL Data Warehouse to Azure Synapse Analytics. We will send advanced notice before the change is made. This change is relevant for customers who parse product name from the result of SELECT @@VERSION in their application code. To avoid application code changes due to product rebranding, please use these commands to query SERVERPROPERTY for the database product name and version:
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> To return version number XX.X.XXXXX.X (without product name) use this command:
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> ```
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> SELECT SERVERPROPERTY('ProductVersion')
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| Service improvements | Details |
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| --- | --- |
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|**Database compatibility level (Preview)**| With this release, users can now set a database’s compatibility level to get the Transact-SQL language and query processing behaviors of a specific version of SQL Analytics engine. For more information, see [sys.database_scoped_configurations](/sql/relational-databases/system-catalog-views/sys-database-scoped-configurations-transact-sql?view=azure-sqldw-latest&branch=pr-en-us-13797) and [Alter Database Scoped Configuration](/sql/t-sql/statements/alter-database-scoped-configuration-transact-sql?view=sql-server-ver15).|
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|**Database compatibility level (Preview)**| With this release, users can now set a database's compatibility level to get the Transact-SQL language and query processing behaviors of a specific version of SQL Analytics engine. For more information, see [sys.database_scoped_configurations](/sql/relational-databases/system-catalog-views/sys-database-scoped-configurations-transact-sql?view=azure-sqldw-latest&branch=pr-en-us-13797) and [Alter Database Scoped Configuration](/sql/t-sql/statements/alter-database-scoped-configuration-transact-sql?view=sql-server-ver15).|
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|**Sp_describe_undeclared_parameters**| Allow users to see the metadata about undeclared parameters in a Transact-SQL batch. For more information, see [sp_describe_undeclared_parameters](/sql/relational-databases/system-stored-procedures/sp-describe-undeclared-parameters-transact-sql?view=sql-server-ver15).|
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## January 2020
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| Service improvements | Details |
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| --- | --- |
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|**Data Discovery & Classification**|Data Discovery & Classification is now available in public preview for SQL Analytics. It’s critical to protect sensitive data and the privacy of your customers. As your business and customer data assets grow, it becomes unmanageable to discover, classify, and protect your data. The data discovery and classification feature that we’re introducing natively with SQL Analytics helps make protecting your data more manageable. The overall benefits of this capability are:<br/>• Meeting data privacy standards and regulatory compliance requirements.<br/>• Restricting access to and hardening the security of data warehouses containing highly sensitive data.<br/>• Monitoring and alerting on anomalous access to sensitive data.<br/>• Visualization of sensitive data in a central dashboard on the Azure portal. </br></br>Data Discovery & Classification is available for SQL Analytics in all Azure regions, It's part of Advanced Data Security including Vulnerability Assessment and Threat Detection. For more information about Data Discovery & Classification, see the [blog post](https://azure.microsoft.com/blog/announcing-public-preview-of-data-discovery-classification-for-microsoft-azure-sql-data-warehouse/) and our online [documentation](/azure/sql-database/sql-database-data-discovery-and-classification).|
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|**Data Discovery & Classification**|Data Discovery & Classification is now available in public preview for SQL Analytics. It's critical to protect sensitive data and the privacy of your customers. As your business and customer data assets grow, it becomes unmanageable to discover, classify, and protect your data. The data discovery and classification feature that we're introducing natively with SQL Analytics helps make protecting your data more manageable. The overall benefits of this capability are:<br/>• Meeting data privacy standards and regulatory compliance requirements.<br/>• Restricting access to and hardening the security of data warehouses containing highly sensitive data.<br/>• Monitoring and alerting on anomalous access to sensitive data.<br/>• Visualization of sensitive data in a central dashboard on the Azure portal. </br></br>Data Discovery & Classification is available for SQL Analytics in all Azure regions, It's part of Advanced Data Security including Vulnerability Assessment and Threat Detection. For more information about Data Discovery & Classification, see the [blog post](https://azure.microsoft.com/blog/announcing-public-preview-of-data-discovery-classification-for-microsoft-azure-sql-data-warehouse/) and our online [documentation](/azure/sql-database/sql-database-data-discovery-and-classification).|
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|**GROUP BY ROLLUP**|ROLLUP is now a supported GROUP BY option in SQL Analytics in Azure Synapse. GROUP BY ROLLUP creates a group for each combination of column expressions. GROUP BY also "rolls up" the results into subtotals and grand totals. The GROUP BY function processes from right to left, decreasing the number of column expressions over which it creates groups and aggregation(s). The column order affects the ROLLUP output and can affect the number of rows in the result set.<br/><br/>For more information on GROUP BY ROLLUP, see [GROUP BY (Transact-SQL)](/sql/t-sql/queries/select-group-by-transact-sql?view=azure-sqldw-latest)
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|**Improved accuracy for DWU used and CPU portal metrics**|SQL Analytics significantly enhances metric accuracy in the Azure portal. This release includes a fix to the CPU and DWU Used metric definition to properly reflect your workload across all compute nodes. Before this fix, metric values were being underreported. Expect to see an increase in the DWU used and CPU metrics in the Azure portal. |
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|**Row Level Security**|We introduced Row-level Security capability back in Nov 2017. We’ve now extended this support to external tables as well. Additionally, we’ve added support for calling non-deterministic functions in the inline table-valued functions (inline TVFs) required for defining a security filter predicate. This addition allows you to specify IS_ROLEMEMBER(), USER_NAME() etc. in the security filter predicate. For more information, please see the examples in the [Row-level Security documentation](/sql/relational-databases/security/row-level-security).|
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|**Row Level Security**|We introduced Row-level Security capability back in Nov 2017. We've now extended this support to external tables as well. Additionally, we've added support for calling non-deterministic functions in the inline table-valued functions (inline TVFs) required for defining a security filter predicate. This addition allows you to specify IS_ROLEMEMBER(), USER_NAME() etc. in the security filter predicate. For more information, please see the examples in the [Row-level Security documentation](/sql/relational-databases/security/row-level-security).|
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|**Additional T-SQL Support**|The T-SQL language surface area for SQL Analytics has been extended to include support for [STRING_SPLIT (Transact-SQL)](/sql/t-sql/functions/string-split-transact-sql).
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|**Query Optimizer enhancements** |Query optimization is a critical component of any database. Making optimal choices on how to best execute a query can yield significant improvements. When executing complex analytical queries in a distributed environment, the number of operations executed matters. Query performance has been enhanced by producing better quality plans. These plans minimize expensive data transfer operations and redundant computations such as, repeated subqueries. For more information, see this Azure Synapse [blog post](https://azure.microsoft.com/blog/smarter-faster-safer-azure-sql-data-warehouse-is-simply-unmatched/).|
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| --- | --- |
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|**Virtual Network Service Endpoints Generally Available**|This release includes general availability of Virtual Network (VNet) Service Endpoints for SQL Analytics in Azure Synapse in all Azure regions. VNet Service Endpoints enable you to isolate connectivity to your logical server from a given subnet or set of subnets within your virtual network. The traffic to Azure Synapse from your VNet will always stay within the Azure backbone network. This direct route will be preferred over any specific routes that take Internet traffic through virtual appliances or on-premises. No additional billing is charged for virtual network access through service endpoints. Current pricing model for [Azure Synapse](https://azure.microsoft.com/pricing/details/sql-data-warehouse/gen2/) applies as is.<br/><br/>With this release, we also enabled PolyBase connectivity to [Azure Data Lake Storage Gen2](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-introduction) (ADLS) via [Azure Blob File System](https://docs.microsoft.com/azure/storage/blobs/data-lake-storage-abfs-driver) (ABFS) driver. Azure Data Lake Storage Gen2 brings all the qualities that are required for the complete lifecycle of analytics data to Azure Storage. Features of the two existing Azure storage services, Azure Blob Storage and Azure Data Lake Storage Gen1 are converged. Features from [Azure Data Lake Storage Gen1](https://docs.microsoft.com/azure/data-lake-store/index), such as file system semantics, file-level security, and scale are combined with low-cost, tiered storage, and high availability/disaster recovery capabilities from [Azure Blob Storage](https://docs.microsoft.com/azure/storage/blobs/storage-blobs-introduction).<br/><br/>Using Polybase you can also import data into SQL Analytics in Azure Synapse from Azure Storage secured to VNet. Similarly, exporting data from Azure Synapse to Azure Storage secured to VNet is also supported via Polybase.<br/><br/>For more information on VNet Service Endpoints in Azure Synapse, refer to the [blog post](https://azure.microsoft.com/blog/general-availability-of-vnet-service-endpoints-for-azure-sql-data-warehouse/) or the [documentation](https://docs.microsoft.com/azure/sql-database/sql-database-vnet-service-endpoint-rule-overview?toc=/azure/sql-data-warehouse/toc.json).|
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|**Automatic Performance Monitoring (Preview)**|[Query Store](https://docs.microsoft.com/sql/relational-databases/performance/monitoring-performance-by-using-the-query-store?view=sql-server-2017) is now available in Preview in SQL Analytics in Azure Synapse. Query Store is designed to help you with query performance troubleshooting by tracking queries, query plans, runtime statistics, and query history to help you monitor the activity and performance of your data warehouse. Query Store is a set of internal stores and Dynamic Management Views (DMVs) that allow you to:<br/><br/>• Identify and tune top resource consuming queries<br/>• Identify and improve unplanned workloads<br/>• Evaluate query performance and impact to the plan by changes in statistics, indexes, or system size (DWU setting)<br/>• See full query text for all queries executed<br/><br/>The Query Store contains three actual stores:<br/>• A plan store for persisting the execution plan information<br/>• A runtime stats store for persisting the execution statistics information<br/>• A wait stats store for persisting wait stats information.<br/><br/>SQL Analytics in Azure Synapse manages these stores automatically and provides an unlimited number of queries storied over the last seven days at no additional charge. Enabling Query Store is as simple as running an ALTER DATABASE T-SQL statement: <br/>sql ----ALTER DATABASE [DatabaseName] SET QUERY_STORE = ON;-------For more information on Query Store, see the article, [Monitoring performance by using the Query Store](/sql/relational-databases/performance/monitoring-performance-by-using-the-query-store), and the Query Store DMVs, such as [sys.query_store_query](/sql/relational-databases/system-catalog-views/sys-query-store-query-transact-sql). Here is the [blog post](https://azure.microsoft.com/blog/automatic-performance-monitoring-in-azure-sql-data-warehouse-with-query-store/) announcing the release.|
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|**Lower Compute Tiers for SQL Analytics**|SQL Analytics in Azure Synapse now supports lower compute tiers. Customers can experience Azure Synapse’s leading performance, flexibility, and security features starting with 100 cDWU ([data warehouse units](what-is-a-data-warehouse-unit-dwu-cdwu.md)) and scale to 30,000 cDWU in minutes. Starting mid-December 2018, customers can benefit from Gen2 performance and flexibility with lower compute tiers in [regions](gen2-migration-schedule.md#automated-schedule-and-region-availability-table), with the rest of the regions available during 2019.<br/><br/>By dropping the entry point for next-generation data warehousing, Microsoft opens the doors to value-driven customers who want to evaluate all the benefits of a secure, high-performance data warehouse without guessing which trial environment is best for them. Customers may start as low as 100 cDWU, down from the current 500 cDWU entry point. SQL Analytics continues to support pause and resume operations and goes beyond just the flexibility in compute. Gen2 also supports unlimited column-store storage capacity along with 2.5 times more memory per query, up to 128 concurrent queries and [adaptive caching](https://azure.microsoft.com/blog/adaptive-caching-powers-azure-sql-data-warehouse-performance-gains/) features. These features on average bring five times more performance compared to the same data warehouse Unit on Gen1 at the same price. Geo-redundant backups are standard for Gen2 with built-in guaranteed data protection. SQL Analytics in Azure Synapse is ready to scale when you are.|
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|**Lower Compute Tiers for SQL Analytics**|SQL Analytics in Azure Synapse now supports lower compute tiers. Customers can experience Azure Synapse's leading performance, flexibility, and security features starting with 100 cDWU ([data warehouse units](what-is-a-data-warehouse-unit-dwu-cdwu.md)) and scale to 30,000 cDWU in minutes. Starting mid-December 2018, customers can benefit from Gen2 performance and flexibility with lower compute tiers in [regions](gen2-migration-schedule.md#automated-schedule-and-region-availability-table), with the rest of the regions available during 2019.<br/><br/>By dropping the entry point for next-generation data warehousing, Microsoft opens the doors to value-driven customers who want to evaluate all the benefits of a secure, high-performance data warehouse without guessing which trial environment is best for them. Customers may start as low as 100 cDWU, down from the current 500 cDWU entry point. SQL Analytics continues to support pause and resume operations and goes beyond just the flexibility in compute. Gen2 also supports unlimited column-store storage capacity along with 2.5 times more memory per query, up to 128 concurrent queries and [adaptive caching](https://azure.microsoft.com/blog/adaptive-caching-powers-azure-sql-data-warehouse-performance-gains/) features. These features on average bring five times more performance compared to the same data warehouse Unit on Gen1 at the same price. Geo-redundant backups are standard for Gen2 with built-in guaranteed data protection. SQL Analytics in Azure Synapse is ready to scale when you are.|
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|**Columnstore Background Merge**|By default, Azure SQL Data stores data in columnar format, with micro-partitions called [rowgroups](sql-data-warehouse-memory-optimizations-for-columnstore-compression.md). Sometimes, due to memory constrains at index build or data load time, the rowgroups may be compressed with less than the optimal size of one million rows. Rowgroups may also become fragmented due to deletes. Small or fragmented rowgroups result in higher memory consumption, as well as inefficient query execution. With this release, the columnstore background maintenance task merges small compressed rowgroups to create larger rowgroups to better utilize memory and speed up query execution.
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