20220927 1901

Author: WilliamDAssafMSFT

articles/synapse-analytics/whats-new.md

@@ -49,7 +49,7 @@ The following table lists the features of Azure Synapse Analytics that have tran
 |**Month** | **Feature** |  **Learn more**|
 |:-- |:-- | :-- |
 | September 2022 | **MERGE T-SQL syntax** | [MERGE T-SQL syntax](/sql/t-sql/statements/merge-transact-sql?view=azure-sqldw-latest&preserve-view=true) has been a highly requested addition to the Synapse T-SQL library. As in SQL Server, the MERGE syntax encapsulates INSERTs/UPDATEs/DELETEs into a single high-performance statement. Available in dedicated SQL pools in version 10.0.17829 and above. For more, see the [MERGE T-SQL announcement blog](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/merge-t-sql-for-dedicated-sql-pools-is-now-ga/ba-p/3634331).|
-| July 2022 | **Apache Spark™ 3.2 on Synapse Analytics** | Apache Spark™ 3.2 on Synapse Analytics is now generally available. Review the [official release notes](https://spark.apache.org/releases/spark-release-3-2-0.html) and [migration guidelines between Spark 3.1 and 3.2](https://spark.apache.org/docs/latest/sql-migration-guide.html#upgrading-from-spark-sql-31-to-32) to assess potential changes to your applications. For more details, read [Apache Spark version support and Azure Synapse Runtime for Apache Spark 3.2](./spark/apache-spark-version-support.md). Highlights of what got better in Spark 3.2 in the [Azure Synapse Analytics July Update 2022](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/azure-synapse-analytics-july-update-2022/ba-p/3535089#TOCREF_1).|
+| July 2022 | **Apache Spark™ 3.2 for Synapse Analytics** | Apache Spark™ 3.2 for Synapse Analytics is now generally available. Review the [official release notes](https://spark.apache.org/releases/spark-release-3-2-0.html) and [migration guidelines between Spark 3.1 and 3.2](https://spark.apache.org/docs/latest/sql-migration-guide.html#upgrading-from-spark-sql-31-to-32) to assess potential changes to your applications. For more details, read [Apache Spark version support and Azure Synapse Runtime for Apache Spark 3.2](./spark/apache-spark-version-support.md). Highlights of what got better in Spark 3.2 in the [Azure Synapse Analytics July Update 2022](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/azure-synapse-analytics-july-update-2022/ba-p/3535089#TOCREF_1).|
 | July 2022 | **Apache Spark in Azure Synapse Intelligent Cache feature** | Intelligent Cache for Spark automatically stores each read within the allocated cache storage space, detecting underlying file changes and refreshing the files to provide the most recent data. To learn more, see how to [Enable/Disable the cache for your Apache Spark pool](./spark/apache-spark-intelligent-cache-concept.md).|
 | June 2022 | **Map Data tool** | The Map Data tool is a guided process to help you create ETL mappings and mapping data flows from your source data to Synapse without writing code. To learn more about the Map Data tool, read [Map Data in Azure Synapse Analytics](./database-designer/overview-map-data.md).|
 | June 2022 | **User Defined Functions** | User defined functions (UDFs) are now generally available. To learn more, read [User defined functions in mapping data flows](https://techcommunity.microsoft.com/t5/azure-data-factory-blog/introducing-user-defined-functions-preview-for-mapping-data/ba-p/3414628). |
@@ -87,7 +87,7 @@ This section summarizes recent new features and capabilities of [Apache Spark fo
 | 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).|
 | May 2022 | **Azure Synapse dedicated SQL pool connector for Apache Spark now available in Python** | Previously, the [Azure Synapse Dedicated SQL Pool Connector for Apache Spark](./spark/synapse-spark-sql-pool-import-export.md) was only available using Scala. Now, [the dedicated SQL pool connector for Apache Spark can be used with Python on Spark 3](https://techcommunity.microsoft.com/t5/azure-synapse-analytics-blog/azure-synapse-analytics-may-update-2022/ba-p/3430970#TOCREF_6). |
 | May 2022 | **Manage Azure Synapse Apache Spark configuration** | With the new [Apache Spark configurations](./spark/apache-spark-azure-create-spark-configuration.md) feature, you can create a standalone Spark configuration artifact with auto-suggestions and built-in validation rules. The Spark configuration artifact allows you to share your Spark configuration within and across Azure Synapse workspaces. You can also easily associate your Spark configuration with a Spark pool, a Notebook, and a Spark job definition for reuse and minimize the need to copy the Spark configuration in multiple places. |
-| April 2022 | **Apache Spark 3.2 on Synapse Analytics** | Apache Spark 3.2 on Synapse Analytics with preview availability. Review the [official Spark 3.2 release notes](https://spark.apache.org/releases/spark-release-3-2-0.html) and [migration guidelines between Spark 3.1 and 3.2](https://spark.apache.org/docs/latest/sql-migration-guide.html#upgrading-from-spark-sql-31-to-32) to assess potential changes to your applications. For more details, read [Apache Spark version support and Azure Synapse Runtime for Apache Spark 3.2](./spark/apache-spark-version-support.md). |
+| April 2022 | **Apache Spark 3.2 for Synapse Analytics** | Apache Spark 3.2 for Synapse Analytics with preview availability. Review the [official Spark 3.2 release notes](https://spark.apache.org/releases/spark-release-3-2-0.html) and [migration guidelines between Spark 3.1 and 3.2](https://spark.apache.org/docs/latest/sql-migration-guide.html#upgrading-from-spark-sql-31-to-32) to assess potential changes to your applications. For more details, read [Apache Spark version support and Azure Synapse Runtime for Apache Spark 3.2](./spark/apache-spark-version-support.md). |
 | April 2022 | **Parameterization for Spark job definition** | You can now assign parameters dynamically based on variables, metadata, or specifying Pipeline specific parameters for the Spark job definition activity. For more details, read [Transform data using Apache Spark job definition](quickstart-transform-data-using-spark-job-definition.md#settings-tab). |
 | April 2022 | **Spark notebook snapshot** | You can access a snapshot of the Notebook when there's a Pipeline Notebook run failure or when there's a long-running Notebook job. To learn more, read [Transform data by running a Synapse notebook](synapse-notebook-activity.md?tabs=classical#see-notebook-activity-run-history) and [Introduction to Microsoft Spark utilities](./spark/microsoft-spark-utilities.md?pivots=programming-language-scala#reference-a-notebook-1). |
 | March 2022 | **Synapse Spark Common Data Model (CDM) connector** | The CDM format reader/writer enables a Spark program to read and write CDM entities in a CDM folder via Spark dataframes. To learn more, see [how the CDM connector supports reading, writing data, examples, & known issues](./spark/data-sources/apache-spark-cdm-connector.md). |