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| June 2022 |**Azure Orbital analytics with Synapse Analytics**| We now offer an [Azure Orbital analytics sample solution](https://github.com/Azure/Azure-Orbital-Analytics-Samples) showing an end-to-end implementation of extracting, loading, transforming, and analyzing spaceborne data by using geospatial libraries and AI models with Azure Synapse Analytics. The sample solution also demonstrates how to integrate geospatial-specific [Azure Cognitive Services](../cognitive-services/index.yml) models, AI models from partners, and bring-your-own-data models. |
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| June 2022 |**Azure Synapse success by design**| The [Azure Synapse proof of concept playbook](./guidance/proof-of-concept-playbook-overview.md) provides a guide to scope, design, execute, and evaluate a proof of concept for SQL or Spark workloads. |
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| May 2022 |**Azure Synapse influencer program**|[Join a community of Azure Synapse Influencers](http://aka.ms/synapseinfluencers) who are helping each other achieve more with cloud analytics! |
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| May 2022 |**Azure Synapse influencer program**|[Join a community of Azure Synapse Influencers](https://aka.ms/synapseinfluencers) who are helping each other achieve more with cloud analytics! |
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## Azure Synapse Analytics Apache Spark
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This section summarizes recent new features and capabilities of [Apache Spark in Azure Synapse Analytics](spark/apache-spark-overview.md).
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|**Month**|**Feature**|**Learn more**|
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| September 2022 |**New query optimization techniques in Apache Spark for Azure Synapse**| Read the [findings from Microsoft's work](http://vldb.org/pvldb/vol15/p936-rajan.pdf) to gain considerable performance benefits across the board on the reference TPC-DS workload as well as a significant reduction in query plan generation time. |
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| September 2022 |**New query optimization techniques in Apache Spark for Azure Synapse**| Read the [findings from Microsoft's work](https://vldb.org/pvldb/vol15/p936-rajan.pdf) to gain considerable performance benefits across the board on the reference TPC-DS workload as well as a significant reduction in query plan generation time. |
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| August 2022 |**Spark elastic pool storage**| Azure Synapse Analytics Spark pools now support elastic pool storage in preview. Elastic pool storage allows the Spark engine to monitor worker nodes temporary storage and attach additional disks if needed. No action is required, and you should see fewer job failures as a result. For more information, see [Blog: Azure Synapse Analytics Spark elastic pool storage is available for public preview](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/azure-synapse-analytics-august-update-2022/ba-p/3535126#TOCREF_8).|
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| August 2022 |**Spark Optimized Write**| Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written by Apache Spark 3 (3.1 and 3.2) and aims to increase individual file size of the written data. To learn more about the usage scenarios and how to enable this preview feature, read [The need for optimize write on Apache Spark](spark/optimize-write-for-apache-spark.md).|
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| July 2022 |**Apache Spark 2.4 enters retirement lifecycle**| With the general availability of the Apache Spark 3.2 runtime, the Azure Synapse runtime for Apache Spark 2.4 enters a 12-month retirement cycle. You should relocate your workloads to the newer Apache Spark 3.2 runtime within this period. Read more at [Apache Spark runtimes in Azure Synapse](spark/apache-spark-version-support.md).|
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| August 2022|**Spark Delta Lake tables in serverless SQL pools**| The ability to for serverless SQL pools to access Delta Lake tables created in Spark databases is in preview. For more information, see [Azure Synapse Analytics shared metadata tables](metadata/table.md).|
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| August 2022|**Multi-column distribution in dedicated SQL pools**| You can now Hash Distribute tables on multiple columns for a more even distribution of the base table, reducing data skew over time and improving query performance. For more information on opting-in to the preview, see [CREATE TABLE distribution options](/sql/t-sql/statements/create-table-azure-sql-data-warehouse#TableDistributionOptions) or [CREATE TABLE AS SELECT distribution options](/sql/t-sql/statements/create-table-as-select-azure-sql-data-warehouse#table-distribution-options).|
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| August 2022|**Distribution Advisor**| The Distribution Advisor is a new preview feature in Azure Synapse dedicated SQL pools Gen2 that analyzes queries and recommends the best distribution strategies for tables to improve query performance. For more information, see [Distribution Advisor in Azure Synapse SQL](sql/distribution-advisor.md).|
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| August 2022 |**Add SQL objects and users in Lake databases**| New capabilities announced for lake databases in serverless SQL pools: create schemas, views, procedures, inline table-valued functions. You can also database users from your Azure Active Directory domain and assign them to the db_datareader role. For more information, see [Access lake databases using serverless SQL pool in Azure Synapse Analytics](metadata/database.md#custom-sql-metadata-objects) and [Create and use native external tables using SQL pools in Azure Synapse Analytics](sql/create-use-external-tables.md).|
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| August 2022 |**Add SQL objects and users in Lake databases**| New capabilities announced for lake databases in serverless SQL pools: create schemas, views, procedures, inline table-valued functions. You can also database users from your Azure Active Directory domain and assign them to the db_datareader role. For more information, see [Access lake databases using serverless SQL pool in Azure Synapse Analytics](metadata/database.md#custom-sql-objects-in-lake-databases) and [Create and use native external tables using SQL pools in Azure Synapse Analytics](sql/create-use-external-tables.md).|
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| June 2022 |**Result set size limit increase**| The [maximum size of query result sets](./sql/resources-self-help-sql-on-demand.md?tabs=x80070002#constraints) in serverless SQL pools has been increased from 200 GB to 400 GB. |
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| May 2022 |**Automatic character column length calculation for serverless SQL pools**| It is no longer necessary to define character column lengths for serverless SQL pools in the data lake. You can get optimal query performance [without having to define the schema](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/azure-synapse-analytics-may-update-2022/ba-p/3430970#TOCREF_4), because the serverless SQL pool will use automatically calculated average column lengths and cardinality estimation. |
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| April 2022 |**Cross-subscription restore for Azure Synapse SQL GA**| With the PowerShell `Az.Sql` module 3.8 update, the [Restore-AzSqlDatabase](/powershell/module/az.sql/restore-azsqldatabase) cmdlet can be used for cross-subscription restore of dedicated SQL pools. To learn more, see [Restore a dedicated SQL pool to a different subscription](sql-data-warehouse/sql-data-warehouse-restore-active-paused-dw.md#restore-an-existing-dedicated-sql-pool-formerly-sql-dw-to-a-different-subscription-through-powershell). This feature is now generally available for dedicated SQL pools (formerly SQL DW) and dedicated SQL pools in a Synapse workspace. [What's the difference?](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/what-s-the-difference-between-azure-synapse-formerly-sql-dw-and/ba-p/3597772)|
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