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Copy file name to clipboardExpand all lines: articles/synapse-analytics/sql-data-warehouse/backup-and-restore.md
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---
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title: Backup and restore - snapshots, geo-redundant
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description: Learn how backup and restore works in Azure Synapse Analytics SQL pool. Use backups to restore your data warehouse to a restore point in the primary region. Use geo-redundant backups to restore to a different geographical region.
Copy file name to clipboardExpand all lines: articles/synapse-analytics/sql-data-warehouse/column-level-security.md
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title: What is column-level security for Azure Synapse?
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description: Column-Level Security allows customers to control access to database table columns based on the user's execution context or group membership, simplifying the design and coding of security in your application, and allowing you to implement restrictions on column access.
Copy file name to clipboardExpand all lines: articles/synapse-analytics/sql-data-warehouse/design-guidance-for-replicated-tables.md
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title: Design guidance for replicated tables
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description: Recommendations for designing replicated tables in SQL Analytics
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services: sql-data-warehouse
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services: synapse-analytics
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author: XiaoyuMSFT
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manager: craigg
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ms.service: sql-data-warehouse
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ms.service: synapse-analytics
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ms.topic: conceptual
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ms.subservice: development
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ms.subservice:
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ms.date: 03/19/2019
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ms.author: xiaoyul
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ms.reviewer: igorstan
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Replicated tables may not yield the best query performance when:
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- The table has frequent insert, update, and delete operations. These data manipulation language (DML) operations require a rebuild of the replicated table. Rebuilding frequently can cause slower performance.
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- The table has frequent insert, update, and delete operations. The data manipulation language (DML) operations require a rebuild of the replicated table. Rebuilding frequently can cause slower performance.
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- The SQL Analytics database is scaled frequently. Scaling a SQL Analytics database changes the number of Compute nodes, which incurs rebuilding the replicated table.
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- The table has a large number of columns, but data operations typically access only a small number of columns. In this scenario, instead of replicating the entire table, it might be more effective to distribute the table, and then create an index on the frequently accessed columns. When a query requires data movement, SQL Analytics only moves data for the requested columns.
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SELECT EnglishProductName
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FROM DimProduct
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WHERE EnglishDescription LIKE'%frame%comfortable%'
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```
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```
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## Convert existing round-robin tables to replicated tables
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If you already have round-robin tables, we recommend converting them to replicated tables if they meet the criteria outlined in this article. Replicated tables improve performance over round-robin tables because they eliminate the need for data movement. A round-robin table always requires data movement for joins.
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If you already have round-robin tables, we recommend converting them to replicated tables if they meet the criteria outlined in this article. Replicated tables improve performance over round-robin tables because they eliminate the need for data movement. A round-robin table always requires data movement for joins.
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This example uses [CTAS](/sql/t-sql/statements/create-table-as-select-azure-sql-data-warehouse) to change the DimSalesTerritory table to a replicated table. This example works regardless of whether DimSalesTerritory is hash-distributed or round-robin.
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RENAME OBJECT [dbo].[DimSalesTerritory_REPLICATE] TO [DimSalesTerritory];
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DROPTABLE [dbo].[DimSalesTerritory_old];
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```
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```
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### Query performance example for round-robin versus replicated
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A replicated table does not require any data movement for joins because the entire table is already present on each Compute node. If the dimension tables are round-robin distributed, a join copies the dimension table in full to each Compute node. To move the data, the query plan contains an operation called BroadcastMoveOperation. This type of data movement operation slows query performance and is eliminated by using replicated tables. To view query plan steps, use the [sys.dm_pdw_request_steps](/sql/relational-databases/system-dynamic-management-views/sys-dm-pdw-request-steps-transact-sql) system catalog view.
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A replicated table does not require any data movement for joins because the entire table is already present on each Compute node. If the dimension tables are round-robin distributed, a join copies the dimension table in full to each Compute node. To move the data, the query plan contains an operation called BroadcastMoveOperation. This type of data movement operation slows query performance and is eliminated by using replicated tables. To view query plan steps, use the [sys.dm_pdw_request_steps](/sql/relational-databases/system-dynamic-management-views/sys-dm-pdw-request-steps-transact-sql) system catalog view.
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For example, in following query against the AdventureWorks schema, the `FactInternetSales` table is hash-distributed. The `DimDate` and `DimSalesTerritory` tables are smaller dimension tables. This query returns the total sales in North America for fiscal year 2004:
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For example, this load pattern loads data from four sources and invokes four rebuilds.
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- Load from source 1.
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Load from source 1.
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- Select statement triggers rebuild 1.
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- Load from source 2.
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Load from source 2.
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- Select statement triggers rebuild 2.
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- Load from source 3.
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- Select statement triggers rebuild 3.
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-[CREATE TABLE AS SELECT (SQL Analytics)](/sql/t-sql/statements/create-table-as-select-azure-sql-data-warehouse)
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For an overview of distributed tables, see [distributed tables](sql-data-warehouse-tables-distribute.md).
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