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Synapse Analytics April Update 2023","conversation":{"__ref":"Conversation:conversation:3807118"},"id":"message:3807118","revisionNum":4,"uid":3807118,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Welcome to the April 2023 Azure Synapse Analytics Update! This month, we have a new ARM template to deploy Azure Data Explorer DB with Cosmos DB connection, as well as additional updates in Apache Spark for Synapse, Synapse Data Explorer, and Data Integration.  \n   \n Read on for more details and don’t forget to watch the video!  ","introduction":"","metrics":{"__typename":"MessageMetrics","views":8850},"postTime":"2023-05-02T08:00:00.214-07:00","lastPublishTime":"2023-05-02T08:00:00.214-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics April Update 2023 \n \n Welcome to the April 2023 Azure Synapse Analytics Update! This month, we have a new ARM template to deploy Azure Data Explorer DB with Cosmos DB connection, as well as additional updates in Apache Spark for Synapse, Synapse Data Explorer, and Data Integration.  \n   \n Read on for more details and don’t forget to watch the video!  \n   \n \n   \n Table of contents \n \n Apache Spark for Synapse \n \n Delta Lake – Low Shuffle Merge \n \n \n Synapse Data Explorer \n \n Ingest data from Azure Events Hub to ADX free tier \n New ARM template to deploy Azure Data Explorer DB with Cosmos DB connection \n New look and feel for Query command bar in ADX Web \n \n \n Data Integration \n \n Capture changed data from Cosmos DB analytical store (Public Preview) \n \n \n \n   \n Apache Spark for Synapse \n Delta Lake – Low Shuffle Merge \n Low Shuffle Merge optimization for Delta tables is now available in Apache Spark 3.2 and 3.3 pools. You can now update a Delta table with advanced conditions using the Delta Lake MERGE command. It can update data from a source table, view, or DataFrame into a target table. The current algorithm of the MERGE command is not optimized for handling unmodified rows. With Low Shuffle Merge optimization, unmodified rows are excluded from expensive shuffling execution and written separately. \n   \n To learn more about this new command, read Low Shuffle Merge optimization on Delta tables.  \n   \n Synapse Data Explorer \n Ingest data from Azure Events Hub to ADX free tier \n Are you looking for a powerful and cost-effective (or possibly even free...) way to analyze large volumes of near real-time streaming data? Azure Data Explorer now supports integration with Events Hub in ADX free tier. \n   \n Events Hub is a big data streaming platform, which can process millions of events per second in near real-time.  Connecting your Event Hub data to Azure Data Explorer is easy and straightforward and can be done in just a few simple steps, using an intuitive \"One-Click\" ingestion wizard.   \n   \n \n   \n To learn more about ingesting data from Azure Events Hub to ADX free tier, read Free Event Hub data analysis with Azure Data Explorer.  \n   \n New ARM template to deploy Azure Data Explorer DB with Cosmos DB connection \n A new ARM template that deploys an Azure Data Explorer DB with a Cosmos DB connection is now available. This vastly simplifies the deployment of an ADX cluster with: \n \n a System Assigned Identity \n a database \n an Azure Cosmos DB account (NoSql) \n an Azure Cosmos DB database \n an Azure Cosmos DB container \n a data connection between the Cosmos DB container and the Kusto database (using the System Assigned identity) \n \n   \n \n   \n To learn more about this new ARM template, read Deploy Azure Data Explorer DB with Cosmos DB connection with ARM Template. \n   \n New look and feel for Query command bar in ADX Web \n The ADX Web Query command bar has undergone a major redesign to provide an improved user experience. The new design is not only visually appealing but also makes it easier and faster for users to access the commands they need. \n   \n To learn more about the new command bar and other ADX Web updates, read ADX Web updates – March 2023.  \n   \n Data Integration \n Capture changed data from Cosmos DB analytical store (Public Preview) \n \n When you perform data integration and ETL processes in the cloud, your jobs can perform better and be more effective when you only read changed data from your source. We are excited to share that Azure Cosmos DB analytical store now supports change data capture (CDC) for Azure Cosmos DB API for NoSQL and Azure Cosmos DB API for MongoDB.   \n   \n Available in Public Preview, this will allow you to efficiently consume continuous and changed (inserted, updated, and deleted) data from the analytical store. Seamlessly integrated with Azure Synapse Analytics and Azure Data Factory, it is a scalable, no-code experience for high data volume and will not consume provisioned RUs or affect the performance of your transactional workloads while providing lower latency and a lower TCO.   \n   \n You can consume incremental analytical store data from a Cosmos DB container using either Azure Synapse Analytics or Azure Data Factory after you enable the Cosmos DB account for Synapse Link and you have enabled analytical store on a new container or an existing container.  \n   \n \n   \n To learn more about capturing Change Data: \n Watch Azure Cosmos DB analytical store Change Data Capture \n Read Capture Changed Data From your Cosmos DB analytical store (Preview) and Now in preview – Change Data Capture (CDC) with Azure Cosmos DB analytical store. \n   \n Thanks for reading! That's all we have for you this month. We look forward to hearing your comments and questions. We'll see you here for the next monthly update! 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This month, we have the General Availability of Multi-Column Distribution and Prive Endpoint support for Cosmos DB to Azure Data Explorer Synapse Link. We also have additional updates in SQL, Apache Spark for Synapse, and Synapse Data Explorer! Read about all of these new features below!  ","introduction":"","metrics":{"__typename":"MessageMetrics","views":14819},"postTime":"2023-04-04T09:00:00.034-07:00","lastPublishTime":"2023-04-04T14:32:27.583-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics March Update 2023  \n \n Welcome to the Azure Synapse Analytics March 2023 update! This month, we have the General Availability of Multi-Column Distribution and Prive Endpoint support for Cosmos DB to Azure Data Explorer Synapse Link. We also have additional updates in SQL, Apache Spark for Synapse, and Synapse Data Explorer! Read about all of these new features below!  \n   \n Don’t forget to check out our companion video on the Azure Synapse Analytics YouTube channel!  \n   \n \n   \n Table of contents \n \n ​SQL \n \n Multi-Column Distribution (Generally Available) \n Greatest and Least TSQL Functions \n \n \n Apache Spark for Synapse \n \n Library management new ability: in-line installation \n \n \n Synapse Data Explorer \n \n ADX Dashboards (Generally Available) \n Private Endpoint support for Cosmos DB to Azure Data Explorer Synapse Link \n Kusto Dashboards Plotly visuals support \n Amazon S3 support in Kusto Web Explorer (KWE)  \n View cluster history in Kusto Data Explorer (KWE)  \n \n \n \n ​SQL \n Multi-Column Distribution (Generally Available) \n We are excited to announce that Multi-Column Distribution (MCD) for Azure Synapse Dedicated SQL pools is now Generally Available! MCD is highly desirable for easing migrations, promotes faster query performance, and reduces data skew.  \n   \n \n   \n You can choose to distribute data on multiple columns to balance the data distribution in your tables and reduce data movement during query execution. Multi-Column distribution will allow you to choose up to eight columns for distribution.  \n   \n To enable MCD, change the database's compatibility level to 50 with this command:  \n   \n   \n   \n ALTER DATABASE SCOPED CONFIGURATION SET DW_COMPATIBILITY_LEVEL = 50; \n   \n   \n   \n Multi-Column Distribution is supported by the following commands:  \n \n CREATE MATERIALIZED VIEW  \n CREATE TABLE  \n CREATE TABLE AS SELECT  \n \n   \n To learn more about setting the database compatibility level, read Alter Database Scoped Configuration.   \n To learn more about MCD, read Multi-Column Distribution for Dedicated SQL pools is now GA!  \n   \n Greatest and Least TSQL Functions \n GREATEST and LEAST functions are now available in for both Azure Synapse Analytics Serverless and Dedicated SQL pools! GREATEST and LEAST are scalar-valued functions and return the maximum and minimum value, respectively, of a list of one or more expressions.   \n   \n These new T-SQL functions will increase your productivity and enhance your experience with Azure Synapse Analytics. Providing the GREATEST developer experience in Azure is the LEAST we can do.  \n   \n To learn more about these functions, read GREATEST (Transact-SQL) and LEAST (Transact-SQL).  \n   \n Apache Spark for Synapse \n Library management new ability: in-line installation \n %pip and %conda are now available in Apache Spark for Synapse! %pip and %conda are commands that can be used on Notebooks to install Python packages.   \n   \n The availability of these commands will increase your productivity and ensure the agility of managing packages on Apache Spark for Synapse during the interactive run of Notebook.  \n   \n '%pip install' is one of the powerful commands that enables you to install new libraries from a public repository like PyPI, install your custom libraries from storage, or install the full list of libraries from an environment specification file to your Notebook Spark session. You can use '%pip show' to investigate the installed library version when you want to retrieve this information during development.   \n   \n To learn more about the full abilities of these magic commands, read Manage session-scoped Python packages through %pip and %conda commands.  \n   \n Synapse Data Explorer \n \n ADX Dashboards (Generally Available) \n Azure Data Explorer Dashboards are now Generally Available!  \n   \n Azure Data Explorer is a fast and highly scalable data exploration service for log and telemetry data. Using Azure Data Explorer web UI, you can explore your data from end-to-end, starting with data ingestion, running queries, and ultimately building dashboards.  \n   \n Each ADX dashboard is a collection of tiles, optionally organized in pages, where each tile has an underlying query and a visual representation. Using the web UI, you can natively export Kusto Query Language (KQL) queries to a dashboard as visuals and later modify their underlying queries and visual formatting as needed. In addition, to ease data exploration, this fully integrated Azure Data Explorer dashboard experience provides improved query and visualization performance.  \n   \n ADX Dashboards, which are part of the Azure Data Explorer web UI, have a user-friendly interface, allowing you to quickly explore and analyze data without the need for extensive technical knowledge. They offer a range of customization options and are designed to handle big data.   \n   \n \n   \n To learn more about ADX Dashboards, read General availability: ADX Dashboards  \n    \n Private Endpoint support for Cosmos DB to Azure Data Explorer Synapse Link \n 2 months ago, we announced the Public Preview of Cosmos DB to Azure Data Explore Synapse Link. This new data connection enables you to ingest a Cosmos DB container in real time.  \n   \n We now support Cosmos DB accounts behind a Private Endpoint or Service Endpoint.  To connect to such an account, you simply add a  Managed Private Endpoint of type DocumentDB (the original name of Cosmos DB) from the Networking pane:  \n   \n \n   \n And fill out the resource details:  \n   \n \n   \n To learn more about Private Endpoint support for Cosmos DB, watch Cosmos DB Private Endpoint Data Connection and read Create a managed private endpoint for Azure Data Explorer.  \n   \n Kusto Dashboards Plotly visuals support \n We recently added support for Plotly graphing library. You can render your data as a Plotly visual and use its power, diversity, and advanced properties.  \n   \n \n   \n For more details on how to leverage Plotly visuals with or without Python, read Plotly visualizations in Azure Data Explorer.  \n   \n Amazon S3 support in Kusto Web Explorer (KWE)  \n You can now ingest data from Amazon S3 seamlessly via the Ingestion Hub in Kusto Web Explorer (KWE).  \n   \n The Ingestion flow can be completed with following selections:  \n \n Source type: Amazon S3  \n Link to Source: Enter the connection string of a bucket / object.   \n \n   \n To learn more about Amazon S3 support, read Storage connection strings. \n   \n View cluster history in Kusto Data Explorer (KWE)  \n It is now easier to track the history of queries and commands run on a Kusto cluster using .show queries and .show commands-and-queries. These commands allow you to view admin commands and queries that have reached a final state.   \n   \n To access and run the set of KQL statements that will let you view the cluster history, simply right click the cluster name in the Connection pane, and select ‘View history’. A new query tab will be opened with the relevant statements.  \n \n   \n   ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7498","kudosSumWeight":2,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTQ1Mjc4NGk0REZCMzkxMjhCMUFEMkUw?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTQ1Njk5MGlGRDQ1QUI4NUE0NkIzMzhG?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTQ1Njk4OWkzMzAyMkJBQTY5OUQxMTQw?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTQ1Njk5MWk3MjhEQzM1REJGQTYxOTM0?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTQ1Njk5N2k2N0IzRTlDQ0JDMzk1NkYw?revision=10\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDc","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg2MTIzLTQ1Njk5M2k3NUZEODREMDVBOEUzMThD?revision=10\"}"}}],"totalCount":7,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3758812":{"__typename":"Conversation","id":"conversation:3758812","topic":{"__typename":"BlogTopicMessage","uid":3758812},"lastPostingActivityTime":"2023-03-07T09:00:00.027-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzU4ODEyLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=5\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzU4ODEyLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=5","title":"swoeng_0-1643231160419.png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"BlogTopicMessage:message:3758812":{"__typename":"BlogTopicMessage","subject":"Azure Synapse Analytics February Update 2023","conversation":{"__ref":"Conversation:conversation:3758812"},"id":"message:3758812","revisionNum":5,"uid":3758812,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Welcome to Azure Synapse Analytics February update! This month, you’ll find sections on UTF-8 and Japanese Collation support, the General Availability of Spark 3.3, and other features in SQL, Spark, and Data Integration. Read on for more details and check out our video!  ","introduction":"","metrics":{"__typename":"MessageMetrics","views":9724},"postTime":"2023-03-07T09:00:00.027-08:00","lastPublishTime":"2023-03-07T09:00:00.027-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics February Update 2023 \n \n Welcome to Azure Synapse Analytics February update! This month, you’ll find sections on UTF-8 and Japanese Collation support, the General Availability of Spark 3.3, and other features in SQL, Spark, and Data Integration. Read on for more details and check out our video!  \n   \n \n   \n Table of contents \n \n SQL \n \n UTF-8 and Japanese Collation support for Dedicated SQL Pools (General Availability) \n SQL Package support for Serverless SQL pools \n \n \n Apache Spark for Synapse \n \n Azure Synapse Runtime for Apache Spark 3.3 (Generally Available) \n Azure Synapse Runtime for Apache Spark 2.4 will be retired as of September 29, 2023 \n Azure Synapse runtime for Apache Spark 3.1 will be retired as of January 26, 2024 \n Increased Azure Synapse Analytics Spark performance in Korea Central, Central India, and Australia Southeast (General Availability) \n \n \n Data Integration \n \n Set pipeline output value (Public Preview) \n \n \n \n   \n SQL \n UTF-8 and Japanese Collation support for Dedicated SQL Pools (General Availability)  \n We are thrilled to announce that support for UTF-8 and Japanese Collations for Azure Synapse Dedicated SQL pools is now Generally Available!   \n   \n UTF-8 stores multilingual characters in data types CHAR and VARCHAR.  When your data has a mix of Latin alphabet characters and other multilingual characters, you will save space and improve performance by using UTF-8.  This can be set as a database-level or column-level default for Unicode string data.   \n   \n To learn more about what UTF-8 is and how it works, read Introducing UTF-8 support for SQL Server  \n   \n To learn more about this announcement, read UTF-8 and Japanese collations support for Dedicated SQL pools is now GA!  \n   \n SQL Package support for Serverless SQL pools  \n Support for SQL Package is finally available for serverless SQL pools! SQL Package is a command-line utility that automates database development deployment tasks. Starting from release 161.8089.0 of SQL Package, you can perform Extract and Publish operations on serverless SQL pools.  \n   \n Use this SQL package to simplify your CI/CD pipelines. You can Extract a database schema and metadata objects from one serverless SQL pool in one environment (for example, development) and Publish them on a different serverless SQL pool in another environment (testing).  \n   \n To learn more about SQL Package support for Serverless SQL pools, read Release notes for SqlPackage.  \n   \n Apache Spark for Synapse  \n Azure Synapse Runtime for Apache Spark 3.3 (Generally Available)  \n Azure Synapse Runtime for Apache Spark 3.3 has been in Public Preview since November 2022. We are excited to announce that after notable improvements in performance and stability, Azure Synapse Runtime for Apache Spark 3.3 now becomes Generally Available and ready for production workloads.    \n   \n The essential changes include features that come from upgrading Apache Spark to version 3.3.1, Delta Lake to version 2.2.0, and Python to 3.10.  \n   \n For additional details, review the Azure Synapse Runtime for Apache Spark 3.3 (GA) official release notes.  \n For a complete list of improvements, review the Apache Spark 3.3 release notes  \n For more details on migration, review the migration guide  \n   \n Azure Synapse Runtime for Apache Spark 2.4 will be retired as of September 29, 2023  \n On 29 September 2023, Azure Synapse runtime for Apache Spark 2.4 will be retired in accordance with the Synapse runtime for Apache Spark lifecycle policy, and any workloads still using it will stop running. Before that date, you'll need to transition your workloads to version 3.2 or 3.3.  \n   \n We recommend the most recent version, 3.3, because it offers significant enhancements such as:  \n \n Improved reliability, performance, and management with an update of Delta to version 2.2. \n An upgrade to the Apache Log4j 2 library to improve security with better support for encryption and secure socket layers.  \n Improved type annotations and new syntax for easier coding with an update of Python to version 3.10.  \n \n   \n We recommend choosing Azure Synapse runtime for Apache Spark version 3.2 or version 3.3 for your workloads that currently use version 2.4 before 29 September 2023 to ensure they continue to run as usual. If you have code that's incompatible with the version you transition to, follow the Spark Migration guide to troubleshoot it.  \n   \n Azure Synapse runtime for Apache Spark 3.1 will be retired as of January 26, 2024  \n On 26 January 2024, Azure Synapse runtime for Apache Spark 3.1 will be retired in accordance with the Synapse runtime for Apache Spark lifecycle policy, and any workloads still using it will stop running. Before that date, you'll need to transition your workloads to version 3.2 or 3.3.  \n   \n We recommend the most recent version, 3.3, because it offers significant enhancements such as:  \n \n Improved reliability, performance, and management with an update of Delta to version 2.2.  \n An upgrade to the Apache Log4j 2 library to improve security with better support for encryption and secure socket layers.  \n Improved type annotations and new syntax for easier coding with an update of Python to version 3.10.  \n \n   \n We recommend choosing Azure Synapse runtime for Apache Spark version 3.2 or version 3.3 for your workloads that currently use version 3.1 before 26 January 2023 to ensure they continue to run as usual. If you have code that's incompatible with the version you transition to, follow the Spark Migration guide to troubleshoot it.  \n   \n Increased Azure Synapse Analytics Spark performance in Korea Central, Central India, and Australia Southeast (General Availability) \n We are always working to improve Azure Synapse Analytics Spark performance. Significant changes are being made that will increase Spark performance by up to 77%.  \n   \n In November, we announced that we are moving your Spark pools to use Azure v5 VMs. We have over 40 regions world-wide and have completed the changes in the first 3 regions: Korea Central, Central India, and Australia Southeast. These Spark performance improvements improved CPU performance, increased temporary SSD throughput, and leveraged higher remote storage IOPS from these Azure v5 VMs.  \n   \n In most cases, there are no actions that are required. After each region is upgraded, your newly created Spark Pools and jobs will complete in less time using v5 VMs. For existing Spark Pools that were created before this service update, they will continue to run on existing v3 VMs.  Once they reach the idle timeout, and are re-created, they will be created with v5 VMs.  You could choose to reduce the node size or the number of nodes if cost savings are more important to you than job completion elapsed time.  \n   \n To learn more about increased performance, read Optimize Apache Spark jobs in Azure Synapse Analytics and  Apache Spark pool configurations in Azure Synapse Analytics  \n   \n Data Integration  \n Set pipeline output value (Public Preview)  \n When building complex workflows in the cloud with Azure Data Factory and Azure Synapse Pipelines, a very common pattern is to separate different workflow branches into child pipelines. Now, we have expanded the Set Variable activity to allow users to set a new system variable, called Pipeline Return Value. This allows you to customize your pipeline return value when communicating between child and parent pipelines.   \n   \n To learn more about this new system variable, read Setting a pipeline return value with UI  ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7938","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzU4ODEyLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=5\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3731285":{"__typename":"Conversation","id":"conversation:3731285","topic":{"__typename":"BlogTopicMessage","uid":3731285},"lastPostingActivityTime":"2023-02-08T08:00:00.036-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=8","title":"swoeng_0-1643231160419.png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwM2lFMUY4M0ExRUJDMDFBRkEw?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwM2lFMUY4M0ExRUJDMDFBRkEw?revision=8","title":"kkschaaf_4-1675453629638.png","associationType":"BODY","width":345,"height":337,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNGlEMUEzMzFDQTNFN0Y2NTA5?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNGlEMUEzMzFDQTNFN0Y2NTA5?revision=8","title":"kkschaaf_5-1675453629644.png","associationType":"BODY","width":1002,"height":700,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNWlGQzIwNEIxQzU5REY4Q0JE?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNWlGQzIwNEIxQzU5REY4Q0JE?revision=8","title":"kkschaaf_6-1675453629646.png","associationType":"BODY","width":352,"height":670,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNmlCMDNCMDFGQkFDQzU2N0U2?revision=8\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNmlCMDNCMDFGQkFDQzU2N0U2?revision=8","title":"kkschaaf_7-1675453629653.png","associationType":"BODY","width":1113,"height":414,"altText":null},"BlogTopicMessage:message:3731285":{"__typename":"BlogTopicMessage","subject":"Azure Synapse Analytics January Update 2023","conversation":{"__ref":"Conversation:conversation:3731285"},"id":"message:3731285","revisionNum":8,"uid":3731285,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Welcome to the January 2023 update for Azure Synapse Analytics! Read on for sections on Apache spark Advisor in Azure Synapse and the new Apache Spark 3.3 runtime, as well as additional updates in Apache Spark for Synapse, Synapse Data Explorer, and Data Integration. sem ","introduction":"","metrics":{"__typename":"MessageMetrics","views":5509},"postTime":"2023-02-08T08:00:00.036-08:00","lastPublishTime":"2023-02-08T08:00:00.036-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics January Update 2023  \n \n Welcome to the January 2023 update for Azure Synapse Analytics! Read on for sections on Apache spark Advisor in Azure Synapse and the new Apache Spark 3.3 runtime, as well as additional updates in Apache Spark for Synapse, Synapse Data Explorer, and Data Integration.  \n   \n \n   \n Table of contents \n \n Apache Spark for Synapse  \n \n Job and API Concurrency Limits for Apache Spark for Synapse  \n Reservation of Executors as part of Dynamic Allocation  \n Azure Synapse Runtime for Apache Spark 3.3 [Public Preview]  \n Spark Advisor for Azure Synapse Notebook [Public Preview]  \n \n \n Synapse Data Explorer \n \n Cosmos DB synapse link to Azure Data Explorer [Public Preview] \n Apache log4j2 sink connector for Azure Data Explorer \n Ingest preexisting Event Hub events to ADX \n Multivariate Anomaly Detection in Azure Data Explorer \n ADX One Click file ingestion just became much more scalable \n ADX Dashboards enhancements: Conditional Formatting \n ADX Dashboards enhancements: Pie Chart displays \n ADX Kusto Web Explorer (KWE) JPath viewer \n \n \n Data Integration \n \n Express virtual network injection for SSIS [Generally Available] \n Flowlets now supports schema drift \n SQL CDC incremental extract now supports numeric columns \n \n \n \n   \n Apache Spark for Synapse  \n Job and API Concurrency Limits for Apache Spark for Synapse  \n \n Azure Synapse Analytics allows users to create and manage Spark Pools in their workspaces thereby enabling key scenarios like data engineering/ data preparation, data exploration, machine learning and streaming data processing workflows. You can create any number of spark pools in their workspaces based on their data processing requirements.  These data processing workflows are orchestrated as spark jobs which are submitted to the selected spark pools in workspace.   \n   \n When you are trying to submit jobs using the Jobs APIs in high traffic scenarios, using a random or constant or exponential time interval for the retries would still result in HTTP 429 failures and will incur in high number of retries. This would result in an increase in the overall time taken for the requests to get accepted by the service. Instead by using the service provided Retry-After value, you would experience higher success rate in job submissions.  \n   \n You can have 1000 active jobs in a single workspace at any given point in time. Active jobs include jobs currently running and jobs that are in queued state.  \n   \n To learn more about job and API concurrency limits enforced across regions, read Concurrency and API rate limits for Apache Spark pools in Azure Synapse Analytics  \n   \n Reservation of Executors as part of Dynamic Allocation  \n \n It’s often hard to size the number of executors for a Spark application as the number of executors required vary across different stages of the executor of a spark job.  With Dynamic Allocation which can be enabled as part of the pool configurations by providing the min and max number of executors the platform automatically allocates executors for the spark application. As part of this dynamic allocation the platform reserves a set of executors for every spark application submitted based to support successful auto scale scenarios to provide users with reliable job execution experience.   \n   To learn more about how reservation of executors works, read Reservation of Executors as part of Dynamic Allocation in Synapse Spark Pools.  \n   \n Azure Synapse Runtime for Apache Spark 3.3 [Public Preview]  \n You can now create Azure Synapse Runtime for Apache Spark 3.3. The essential changes include features which come from upgrading Apache Spark to version 3.3.1 and upgrading Delta Lake. Please review the official release notes for Apache Spark 3.3.0 and Apache Spark 3.3.1 to check the complete list of fixes and features. In addition, review the migration guidelines between Spark 3.2 and 3.3 to assess potential changes to your applications, jobs, and notebooks.   \n   \n To learn more about Azure Synapse Runtime, read Azure Synapse Runtime for Apache Spark 3.3 components versions and Azure Synapse Runtime for Apache Spark 3.3 is now in Public Preview.  \n   \n Spark Advisor for Azure Synapse Notebook [Public Preview]  \n \n Synapse Spark Advisor analyzes code run by Spark and displays real-time advice for Notebooks. The Spark advisor offers recommendations for code optimization based on built-in common patterns, performs error analysis, and locates the root cause of failures.   \n   \n To learn more about Spark Advisor, read Apache Spark Advisor in Azure Synapse Analytics (Preview) \n   \n Synapse Data Explorer \n Cosmos DB synapse link to Azure Data Explorer [Public Preview] \n Azure Cosmos DB is a fully managed NoSQL distributed database for web, mobile, gaming, and IoT application that needs to handle massive amounts of data, reads, and writes at a global scale with near-real response times.  Azure Data Explorer (ADX) is a big data analytics platform. It can scale to petabytes of data, is optimized for time series, and supports structured, semi-structured (JSON) and unstructured (text) data. In addition, it has advanced geo-spatial capabilities. Those two technologies greatly complement each other, and they can now do so in a fully managed way with the new Cosmos DB to Azure Data Explorer Synapse Link. ADX native ingestion of Cosmos DB brings the high-throughput / low-latency transactional Cosmos DB data to the analytical world of Kusto, delivering the best of both worlds. Data can be ingested in near real-time (streaming ingestion) to run analytics on the most current data or audit changes. \n   \n \n   \n To learn more about Cosmos DB Synapse Link for ADX, read Ingest data from Azure Cosmos DB into Azure Data Explorer (Preview) \n   \n Apache log4j2 sink connector for Azure Data Explorer \n New year, new Kusto connector! Log4j2 allows you to easily stream your log data to Azure Data Explorer, where you can analyze, visualize, and alert on your logs in real-time. \n   \n Log4j is a popular logging framework for Java applications maintained by the Apache Foundation. Log4j allows developers to control which log statements are output with arbitrary granularity based on the logger's name, logger level, and message pattern. One of the options for storing log data is to send it to a managed data analytics service, such as Azure Data Explorer (ADX).  \n   \n The Log4j2-ADX connector is an open-source connector that was developed to easily send Log4J2 data to ADX.  In the Log4j2-ADX connector, we created KustoStrategy (a custom strategy) to be used in the RollingFileAppender, which can be configured to connect to the ADX cluster. Logs are written into the rolling file to prevent any data loss arising out of network failure while connecting to the ADX cluster. The data is safely stored in a rolling file and then flushed to the ADX cluster. \n   \n Log4j2-ADX connector also provides a demo/sample application that can be used to quickly get started with producing logs that can be ingested into the ADX cluster.  \n   \n To learn more about the log4j2 connector, read Getting started with Apache Log4j and Azure Data Explorer.  \n   \n Ingest preexisting Event Hub events to ADX \n ADX can now ingest Event Hub data that existed before the creation of an Event Hub data connection in your ADX cluster. You can use this feature by entering the Event retrieval start date (under \"Advanced settings\") on the ADX's Event Hub data connection page of Azure portal, on the Kusto Web UI ingestion wizard (One-Click), or by using the properties.retrievalStartDate of the data connection REST API. \n   \n \n   \n To learn more about ingesting preexisting Event Hub events to ADX, read Connect to the event hub,  Use the ingestion wizard to create an Azure Event Hubs data connection for Azure Data Explorer, and Data Connections - Create Or Update. \n   \n Multivariate Anomaly Detection in Azure Data Explorer \n ADX contains native support for detecting anomalies over multiple time series by using the function series_decompose_anomalies(). This function can analyze thousands of time series in seconds, enabling near real time monitoring solutions and workflows based on ADX. Univariate analysis is simpler, faster, and easily scalable and is applicable to most real-life scenarios. However, there are some cases where it might miss anomalies that can only be detected by analyzing multiple metrics at the same time. \n   \n For some scenarios, there might be a need for a true multivariate model, jointly analyzing multiple metrics. This can be achieved now in ADX using the new Python-based multivariate anomaly detection functions. \n   \n To support multivariate anomaly detection, we have introduced three new UDFs (User Defined Functions) that apply different multivariate models on ADX time series. These functions are based on models from scikit-learn (the most common Python ML library), taking advantage of ADX capability to run inline Python as part of the KQL query.   \n   \n To learn more about how Multivariate Anomaly Detection is performed in ADX, read Multivariate Anomaly Detection in Azure Data Explorer. \n   \n To learn more about anomaly detection and forecasting, read Anomaly detection & forecasting in Azure Data Explorer. \n   \n ADX One Click file ingestion just became much more scalable \n The ADX One click file upload now supports up to 1000 files (previously 10) at one go. This will be handy for users who have many small files and would like to complete the ingestion process at one go. \n   \n To learn more about ADX one click file ingestion, read Ingest data wizard. \n   \n ADX Dashboards enhancements: Conditional Formatting \n Conditional formatting helps in surfacing anomalies or just interesting data points in a result set. We revamped our conditional formatting component in table, stat, and multi stat visuals to include much more. Now you can either format a visual by using conditions or by applying themes to numeric columns or discrete values to non-numeric ones. The formatting can be applied to a specific column or to the entire row. \n   \n \n   \n To learn more about conditional formatting, read Conditional formatting \n   \n ADX Dashboards enhancements: Pie Chart displays \n New display options supported for Pie chart visualization in Dashboards allow you to focus on the data you care about. By selecting the column by which you order by and the top N values, you can get a clearer picture of the data distribution. \n   \n \n   \n To learn more about Pie Chart displays, read Pie chart \n   \n ADX Kusto Web Explorer (KWE) JPath viewer \n JPath notation describes the path to one or more elements in a JSON document. Using the JPath viewer in a result’s expanded view allows you to quickly get a specific element of a JSON text and easily copy its path expression. This is extremely useful when performing investigation of data (i.e., query results) that contain dynamic fields. \n   \n To learn more about JPath viewer, read JPath viewer. \n   \n Data Integration \n Express virtual network injection for SSIS [Generally Available] \n Express virtual network injection for SSIS is now Generally Available. This method of injecting your SSIS integration runtime into a virtual network allows for your SSIS integration runtime provisions and starts faster. Inbound traffic is also not needed to meet Enterprise security compliance requirements. \n   \n To learn more about express virtual network injection for SSIS, read General Availability of Express Virtual Network injection for SSIS in Azure Data Factory. \n   \n Flowlets now supports schema drift \n Allow schema drift is now supported for flowlets. Flowlets are reusuable activities that can be created from scratch or from an existing mapping dataflow. Now, with schema drift support, users can use their flowlets for datasets that have changing source columns, allowing for more flexible flowlet use across many mapping dataflows.  This is easily enabled in source and sink by selecting Allow schema drift under options. \n   \n To learn more about schema drift, read Schema drift in mapping data flow. \n   \n SQL CDC incremental extract now supports numeric columns \n Enabling incremental extract in SQL sources in mapping dataflows allows you to only process rows that have changed since the last time that pipeline was executed. Previously, when enabling incremental extract, users were only allowed to select a date/time column as a watermark. Now, supported incremental column types include date/time and numeric columns. \n   \n To learn more about change data capture, read Auto incremental extraction in mapping data flow. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"13212","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwM2lFMUY4M0ExRUJDMDFBRkEw?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNGlEMUEzMzFDQTNFN0Y2NTA5?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNWlGQzIwNEIxQzU5REY4Q0JE?revision=8\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzMxMjg1LTQzODYwNmlCMDNCMDFGQkFDQzU2N0U2?revision=8\"}"}}],"totalCount":5,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3680019":{"__typename":"Conversation","id":"conversation:3680019","topic":{"__typename":"BlogTopicMessage","uid":3680019},"lastPostingActivityTime":"2023-01-23T14:33:36.923-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjgwMDE5LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjgwMDE5LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=6","title":"swoeng_0-1643231160419.png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"BlogTopicMessage:message:3680019":{"__typename":"BlogTopicMessage","subject":"Azure Synapse Analytics November Update 2022","conversation":{"__ref":"Conversation:conversation:3680019"},"id":"message:3680019","revisionNum":6,"uid":3680019,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Welcome to the November 2022 update for Azure Synapse Analytics! This month, you’ll find sections on increased Spark performance, the new Kusto Emulator, as well as additional updates in Apache Spark for Synapse, Synapse Data Explorer, and Machine Learning.  ","introduction":"","metrics":{"__typename":"MessageMetrics","views":7109},"postTime":"2022-11-30T08:00:00.047-08:00","lastPublishTime":"2023-01-23T14:33:36.923-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics November Update 2022 \n \n Welcome to the November 2022 update for Azure Synapse Analytics! This month, you’ll find sections on increased Spark performance, the new Kusto Emulator, as well as additional updates in Apache Spark for Synapse, Synapse Data Explorer, and Machine Learning. \n \n \n \n Table of contents \n \n Apache Spark for Synapse \n \n Increasing Spark performance \n \n \n Synapse Data Explorer \n \n ADX Emulator \n Ingesting files from AWS S3 \n Azure Stream Analytics ADX output [Generally Available] \n Open Telemetry exporter \n Streaming support in Telegraf connector \n Protobuf support in Kafka sink \n Leader follower discoverability \n Aliasing follower databases \n Parse-kv operator \n Scan operator \n \n \n Machine Learning \n \n R Support [Public Preview] \n \n \n \n Apache Spark for Synapse \n Increasing Spark performance \n We are always working to improve Azure Analytics Spark performance. We are making significant changes that will increase Spark performance by up to 77%.  \n \n Based on our testing using the 1TB TPC-H industry benchmark, you're likely to see up to 77% increased performance. While your workload may perform differently than the TPC-H benchmark, everyone is expected to see improved performance. These Spark performance improvements come from moving to the latest Azure v5 VMs which have improved CPU performance, increased temporary SSD throughput, and lastly higher remote storage IOPS.  \n \n We have over 40 regions worldwide and will be implementing this change region by region. Canada Central will be the first region we implement. We expect these changes to take many months to roll out worldwide. We will publish each region that we update, and customers will automatically receive the performance increase in each region at no cost.  \n \n There are no actions that are required. After each region is upgraded, your jobs will complete in less time. You could choose to reduce the node size or the number of nodes if cost savings are more important to you than job completion elapsed time. \n \n To learn more about the increase in Spark performance, read optimizing Spark performance and Apache Spark pool configurations \n \n Synapse Data Explorer \n ADX Emulator \n The ADX Emulator is a Docker Image exposing an ADX Query Engine endpoint. You can use it to create databases and ingest and query data. The emulator understands Kusto Query Language (KQL) the same way the Azure Service does. We can therefore use it for local development and be ensured the code is going to run the same in an Azure Data Explorer cluster. We can also deploy it in a CI/CD pipeline to run automated test suites to ensure our code behaves as expected. \n \n To learn more about the Emulator, read ADX Emulator and watch Kusto Emulator on YouTube \n \n Ingesting files from AWS S3 \n Amazon S3 is one of the most popular object storage services. AWS Customers use Amazon S3 to store data for a range of use cases, such as data lakes, websites, mobile applications, backup and restore, archive, applications, IoT devices, log analytics, and big data analytics. \n \n With the native S3 ingestion support in ADX, customers can bring data from S3 natively without relying on complex ETL pipelines. Customers can also create a continuous data ingestion pipeline to bring data from S3. \n \n To learn more about ingesting files from AWS S3, read Azure Data Explorer supports native ingestion from Amazon S3 \n \n Azure Stream Analytics ADX output [Generally Available] \n Azure Data Explorer output for Azure Stream Analytics is now Generally Available. ASA-ADX output has been available in Preview since last year. Customers can build powerful real time analytics architecture by leveraging ASA and ADX together. With this new integration, Azure Stream Analytics job can natively ingest the data into Azure Data Explorer and Synapse Data Explorer tables. \n \n To learn more about Azure Stream Analytics ADX output, read about the output plugin set up and ASA-ADX common use cases. \n \n Open Telemetry exporter \n OpenTelemetry (OTel) is a vendor-neutral open-source observability framework for instrumenting, generating, collecting, and exporting telemetry data such as traces, metrics, and logs. \n \n ADX OpenTelemetry exporter supports the ingestion of data from many receivers into Azure Data Explorer, allowing customers to instrument, generate, collect, and store data using a vendor-neutral open-source framework. \n \n To learn more Open Telemetry exporter, read Ingest data from OpenTelemetry to Azure Data Explorer \n \n Streaming support in Telegraf connector \n Telegraf is an open source, lightweight, minimal memory footprint agent for collecting, processing, and writing telemetry data including logs, metrics, and IoT data. The Azure Data Explorer output plugin serves as the connector from Telegraf and supports ingestion of data from many types of input plugins into Azure Data Explorer. \n \n We have added support for \"managed\" steaming ingestion in Telegraf, which defaults to streaming ingestion providing latency up to a second when the target table is streaming enabled, with a fallback to batched or queued ingestion. \n \n To learn more Telegraf, read Ingest data from Telegraf into Azure Data Explorer \n \n Protobuf support in Kafka sink \n Protocol buffers (Protobuf) are a language and platform-neutral extensible mechanism for serializing and deserializing structured data for use in communications protocols and data storage. Azure Data Explorer Kafka sink - a gold certified Confluent connector - helps ingest data from Kafka to Azure Data Explorer. We have added Protobuf support in the connector to help customers bring Protobuf data into ADX. \n \n To learn more Protobuf support, read Ingesting Protobuf data from Kafka to Azure Data Explorer. \n \n Leader follower discoverability \n We have enhanced the discoverability of leader & follower databases in your ADX clusters. You can visit the database blade in Azure portal to easily identify all the follower databases following a leader, and the leader for a given follower. The details pane also provides granularity around which specific tables, external tables, and Materialized views have been included or excluded. \n \n To learn more about leader follower discoverability, read Use follower databases \n \n Aliasing follower databases \n The follower database feature allows you to attach a database located in a different cluster to your Azure Data Explorer cluster. Prior to aliasing capability, a database named DB created on the follower cluster took precedence over a database with the same name that was created on the leader cluster, not allowing databases with same name to co-exist. But now you can override the database name while establishing a follower relationship. This allows you to follow multiple databases with the same name from multiple leader clusters or even just make a database available to users with a more user-friendly name. \n \n You can either use a databaseNameOverride property to provide a new follower database name or use databaseNamePrefix when following an entire cluster to add a prefix to all of the databases original names from leader cluster. \n \n To learn more about aliasing follower databases, read Attached Database Configurations - Create Or Update \n For usage code samples, see Use follower databases \n \n Parse-kv operator \n A new operator which extracts structured information from a string expression and represents the information in a key/value form. \n \n The following extraction modes are supported: \n \n Specified delimeter: Extraction based on specified delimiters that dictate how keys/values and pairs are separated from each other. \n Non-specified delimeter: Extraction with no need to specify delimiters. Any non-alphanumeric character is considered a delimiter. \n Regex: Extraction based on RE2 regular expression. \n \n \n To learn more about the new operator, read parse-kv operator. \n \n Scan operator \n This powerful operator enables efficient and scalable process mining and sequence analytics and user analytics in ADX. The user can define a linear sequence of events and ‘scan’ will quickly extract all sequences of those events. Common scenarios for using ‘scan’ include preventive maintenance for IoT devices, customers funnel analysis, recursive calculation, security scenarios looking for known attack steps and more. \n \n To learn more about the new operator, read scan operator. \n \n Machine Learning \n \n R Support [Public Preview] \n Azure Synapse Analytics now provides built-in R support for Apache Spark; this capability is currently in public preview. The R language enables data scientists to apply the industry standard R language to process data and develop ML models and do analysis of their data. Data scientists and analysts can now leverage R in Azure Synapse Analytics through the following capabilities: \n \n \n Azure Synapse Analytics R runtime: Azure Synapse Analytics supports an R runtime that features many popular open-source R packages. \n Access Apache Spark through R: Azure Synapse Notebooks also include support for SparkR and SparklyR, which allows users to interact with Spark using familiar Spark or R interfaces. \n Import custom R packages: Users can standardize the R packages on an Azure Synapse Apache Spark pool by uploading the package as a workspace package. \n Install session-scoped packages: When doing interactive data analysis or machine learning, you might try newer packages, or you might need packages that are currently unavailable on your Apache Spark pool. Instead of updating the pool configuration, users can now use the familiar R syntax to add, manage, and update session dependencies. \n \n \n With the new R support, you can install an R library from CRAN and CRAN snapshots. In the example below, Highcharter is a popular package for R visualizations. You can install this package on all nodes within your Apache Spark pool using the following command: \n \n \n \n install.packages(\"highcharter\", repos = https://cran.microsoft.com/snapshot/2021-07-16/) \n \n \n \n \n Another feature is that you can create a SparkR dataframe using the Spark Data Source API using the following code: \n \n \n \n # Read a csv from ADLSg2 \n\ndf <- read.df('abfss://<container name>@<storage account name>.dfs.core.windows.net/<file name>.csv', 'csv', header=\"true\") \n\nhead(df) \n \n \n \n \n To learn more about how you can start leveraging R in Synapse, read Use R for Apache Spark with Azure Synapse Analytics 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Synapse Analytics January Update 2022","conversation":{"__ref":"Conversation:conversation:3071681"},"id":"message:3071681","revisionNum":24,"uid":3071681,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:442549"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n   \n Welcome to the Azure Synapse January 2022 update! Our first blog of the year includes newly added database templates, a security whitepaper, and data integration updates. For the first time, we will also feature a companion video that you can watch to get the quick key updates. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":15109},"postTime":"2022-01-26T14:39:06.121-08:00","lastPublishTime":"2023-01-16T11:46:41.326-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics January Update 2022 \n   \n Welcome to the Azure Synapse January 2022 update! Our first blog of the year includes newly added database templates, a security whitepaper, and data integration updates. For the first time, we will also feature a companion video that you can watch to get the quick key updates. \n   \n \n   \n Help us improve this monthly blog, we would love to hear back from you on how best to engage and inform you! Leave a comment below. \n   \n Table of contents \n \n Apache Spark for Synapse \n \n 4 New database templates [Public Preview] \n \n \n Machine Learning \n Security \n \n Azure Synapse Analytics security overview \n TLS 1.2 required for new workspaces \n \n \n Data Integration \n \n Data quality validation rules using Assert transformation \n Native data flow connector for Dynamics \n IntelliSense and auto-complete added to pipeline expressions \n \n \n Synapse SQL \n \n COPY schema discovery for complex data ingestion \n HASHBYTES easily generates hashes in Serverless SQL \n \n \n \n \n   \n   \n Apache Spark for Synapse \n 4 New database templates [Public Preview] \n We’ve seen a lot of enthusiasm and adoption of the 11 Synapse database templates during public preview that 4 additional templates have been recently added. You can now access Automotive, Genomics, Manufacturing, and Pharmaceuticals templates in Azure Synapse. See them in either in the gallery or by creating a new lake database from the tab and selecting + Table and then From template. \n   \n \n   \n Learn more by reading Four Additional Azure Synapse Database Templates Now Available in Public Preview \n   \n   \n Machine Learning \n Improvements to the SynapseML library \n   \n \n   \n The release of the Synapse ML library v0.9.5 (previously called MMLSpark) simplifies the creation of massively scalable machine learning pipelines with Apache Spark. It unifies several existing ML Frameworks and new Microsoft algorithms in a single, scalable API that’s usable across Python, R, Scala, and Java. This update includes support for the following new capabilities: \n \n Geospatial Intelligence: Quickly apply the Azure Maps API to solve problems that require geospatial intelligence. Use distributed tools such as geocoding to make sense of informal location data at scale. Learn more about Azure maps on Spark overview. \n Custom Multivariate Anomaly Detection: Train custom multivariate anomaly detection systems within your databases with only a few lines of python. Learn more about the Multivariate Anomaly Detection Python code. \n Healthcare Analytics: Parse and reason about medical text using the parallelism of your Spark cluster. Extract medications, doses, medical relationships, and more. Learn an example usage code for Cognitive Services on Spark overview. \n Responsible AI at Scale: Understand the predictions of opaque-box models, measure dataset bias, and probe models with Individual Conditional Expectation plots. Users can explore model biases with the new Individual Conditional Expectation transformer in our Adult Census Dataset example. Learn more about the Adult Census Dataset example. \n Text to Speech: Use neural voice synthesis to generate thousands of hours of lifelike speech in minutes. Please see our cognitive service overview for example usage. \n \n   \n Learn more by reading the full release notes or visit the SynapseML homepage to get started. \n   \n   \n Security \n Azure Synapse Analytics security overview \n We just published a white paper that explains Synapse's enterprise-grade security capabilities and industry-leading features that addresses security concerns and provides a comprehensive overview of Azure Synapse Analytics security features. This whitepaper covers the five layers of security: Authentication, Access Control, Data Protection, Network Security, and Threat Protection. Use this reference document to understand each security feature and to implement an industry-standard security baseline to protect your data on the cloud. \n   \n Learn more by reading Azure Synapse Analytics security white paper: Introduction \n   \n TLS 1.2 required for new workspaces \n Starting in December 2021, TLS 1.2 is required for newly created Synapse Workspaces. TLS 1.2 provides enhanced security to safeguard against exploits. Login attempts to newly created Synapse workspace from connections using a TLS versions lower than 1.2 will fail. \n   \n Learn more by reading Azure Synapse Analytics connectivity settings \n   \n   \n Data Integration \n Data quality validation rules using Assert transformation \n You can now easily add data quality, data validation, and schema validation to your Synapse ETL jobs by leveraging Assert transformation in Synapse data flows. Add expectations to your data streams that will execute from the pipeline data flow activity to evaluate whether each row or column in your data meets your assertion. Tag the rows as pass or fail and add row-level details about how a constraint has been breached. This is a critical new feature to an already effective ETL framework to ensure that you are loading and processing quality data for your analytical solutions. \n   \n \n   \n Learn more by reading Assert transformation in mapping data flow \n   \n Native data flow connector for Dynamics \n Synapse data flows can now read and write data directly to Dynamics through the new data flow Dynamics connector. Create data sets in data flows to read, transform, aggregate, join, etc., and then write the data back into Dynamics using the built-in Synapse Spark compute. \n   \n Learn more by reading Native data flow connector for Dynamics \n   \n IntelliSense and auto-complete added to pipeline expressions \n It’s here! This much anticipated update adds IntelliSense to expression editing, making it super easy for you to create new expressions, check your expression syntax, find functions, and add code to your pipelines. \n   \n \n   \n Learn more by reading IntelliSense support in Expression Builder for more productive pipeline authoring experiences \n   \n   \n Synapse SQL \n COPY schema discovery for complex data ingestion \n Automatic schema discovery along with auto-table creation process makes it easy for customers to automatically map and load complex data types from Parquet files, such as arrays, and maps into Dedicated SQL pools in Synapse. Rowgroup compression is automatically enabled when customers enable the auto-create table option within the COPY command. Start taking advantage of all these features today to simplify data ingestion with Azure Synapse Analytics! \n Learn more by reading how Github leveraged this functionality in Introducing Automatic Schema Discovery with auto table creation for complex datatypes \n   \n HASHBYTES easily generates hashes in Serverless SQL \n SQL pools now support the HASHBYTES function! HASHBYTES is a T-SQL function which hashes values. This means that you can use the HASHBYTES function in queries that read data using external tables and the OPENROWSET function. \n   \n SELECT\n TOP 100\n HASHBYTES('sha2_256', vendorid) as hashedVendorID,\n vendorID\nFROM\n OPENROWSET(\n BULK 'https://azureopendatastorage.blob.core.windows.net/nyctlc/yellow/puYear=2019/puMonth=1/*.parquet',\n FORMAT = 'parquet'\n ) AS [result]; \n   \n \n   \n   \n The HASHBYTES function is returning the hashes of its input values in SQL and is now supporting the following types: MD2, MD4, MD5, SHA, SHA1, SHA2. \n   \n Learn more by reading about HASHBYTES ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"7674","kudosSumWeight":11,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMDcxNjgxLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=24\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMDcxNjgxLTM0Mjc1MGlDRDdBNjU1QzhBRDY1NEZE?revision=24\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMDcxNjgxLTM0Mjc1MWk1RTAwNEQzRjM3RThEQzJD?revision=24\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMDcxNjgxLTM0Mjc1NWkxRUZCQjE4Mjc4MDg2OTg2?revision=24\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMDcxNjgxLTM0Mjc2M2k2REExMzhENzdFRDgwMDM1?revision=24\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zMDcxNjgxLTM0Mjg5OWk4MzMzNkI4MjNERTk3MTdB?revision=24\"}"}}],"totalCount":6,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3698299":{"__typename":"Conversation","id":"conversation:3698299","topic":{"__typename":"BlogTopicMessage","uid":3698299},"lastPostingActivityTime":"2023-01-16T11:44:29.483-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4Mjk5LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=9\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4Mjk5LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=9","title":"swoeng_0-1643231160419.png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"BlogTopicMessage:message:3698299":{"__typename":"BlogTopicMessage","subject":"Azure Synapse Analytics December Update 2022 - Year End!","conversation":{"__ref":"Conversation:conversation:3698299"},"id":"message:3698299","revisionNum":9,"uid":3698299,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n We want to thank each and every one of you for being a part of the Azure Synapse Community! 2022 was a big year for us. Check out the blog to see some of our highlights from the year and our holiday send-off to you! ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3856},"postTime":"2022-12-20T08:00:00.027-08:00","lastPublishTime":"2023-01-16T11:44:29.483-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" We want to thank each and every one of you for being a part of the Azure Synapse Community! 2022 was a big year for us. Here are some of our highlights:  \n   \n \n Grew our YouTube channel to over 3,600 subscribers \n Launched the Azure Synapse Influencer Program, which has over 800+ members  \n Engaged with over 4,800 followers on our Azure Synapse Analytics Twitter \n Published 90+ blogs, including the monthly update!  \n Started 6 series and produced over 70 videos on the Azure Synapse Analytics YouTube channel  \n Hosted 13 Azure Data Explorer 101 videos for a series on the Azure Data Factory YouTube channel  \n \n   \n The Azure Synapse team and our collaborators wanted to take this month to thank YOU for all that you do for us! Check out the video!  \n   \n \n   \n Our team is hard at work on new and exciting content for 2023. Stay tuned – we'll see you next year!  ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"939","kudosSumWeight":4,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNjk4Mjk5LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=9\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3535136":{"__typename":"Conversation","id":"conversation:3535136","topic":{"__typename":"BlogTopicMessage","uid":3535136},"lastPostingActivityTime":"2022-11-09T09:13:46.744-08:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTM2LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=6\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTM2LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=6","title":"swoeng_0-1643231160419.png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"BlogTopicMessage:message:3535136":{"__typename":"BlogTopicMessage","subject":"Azure Synapse Analytics October Update 2022 - Microsoft Ignite Recap","conversation":{"__ref":"Conversation:conversation:3535136"},"id":"message:3535136","revisionNum":6,"uid":3535136,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Welcome to the October 2022 update for Azure Synapse Analytics! This month's update focuses on features released at Microsoft Ignite 2022, breaking from the traditional editions. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":5947},"postTime":"2022-10-31T09:30:00.049-07:00","lastPublishTime":"2022-10-31T09:30:00.049-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics October Update 2022  \n \n Welcome to the October 2022 update for Azure Synapse Analytics! This month's update focuses on features released at Microsoft Ignite 2022, breaking from the traditional editions. \n   \n Don’t forget to check out our companion video on the Azure Synapse Analytics YouTube channel! \n   \n \n   \n Table of contents \n \n Data Integration \n \n New Microsoft 365 Pipeline Template Dataflows in Preview \n SAP Change Data Capture (CDC) Connector is now General Available \n \n \n Apache Spark\n \n R language support with key library management capabilities is now in preview \n \n \n \n   \n Data Integration \n New Microsoft 365 Pipeline Template Dataflows in Preview \n A new pipeline template for Microsoft 365 data, coming soon in preview, will simplify the configuration experience by enabling a one-click experience to set up Mapping Data Flows. This new feature will eliminate the extra steps needed to connect Microsoft 365 source data for analytics, making it easier for you to configure an always synchronized and compliant integration. To learn more about it: Copy and transform data from Microsoft 365 (Office 365) - Azure Data Factory & Azure Synapse. \n   \n SAP Change Data Capture (CDC) Connector is now General Available \n We launched the general availability of SAP Change Data Capture (CDC) in Azure Data Factory (ADF) and Azure Synapse Analytics. Combining a new data connector with predefined data flow templates, this solution streamlines the integration of SAP data within core Azure services like Azure Synapse Analytics and Azure Machine Learning. To learn more read in the official announcement blog for the SAP CDC connector. \n   \n Apache Spark \n R language support with key library management capabilities is now in preview \n Azure Synapse Analytics provides built-in R support for Apache Spark. As part of this, data scientists can leverage Azure Synapse Analytics notebooks to write and run their R code. This also includes support for SparkR and SparklyR, which allows users to interact with Spark using familiar Spark or R interfaces. To learn more read the official how-to Use R for Apache Spark with Azure Synapse Analytics (Preview). \n   \n   ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2321","kudosSumWeight":4,"repliesCount":2,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTM2LTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=6\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3535133":{"__typename":"Conversation","id":"conversation:3535133","topic":{"__typename":"BlogTopicMessage","uid":3535133},"lastPostingActivityTime":"2022-09-29T12:45:59.350-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTM0Mjc3MWkwRTlGMDc4MEZEMTdDODRB?revision=12","title":"swoeng_0-1643231160419.png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4M2kxREQxRUQzREMyRjk0RTQy?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4M2kxREQxRUQzREMyRjk0RTQy?revision=12","title":"thynguyen_1-1664297181652.png","associationType":"BODY","width":877,"height":525,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4OWlFRDQ3RUUwQjY3RUJBQzlG?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4OWlFRDQ3RUUwQjY3RUJBQzlG?revision=12","title":"MERGE_visual.gif","associationType":"BODY","width":1446,"height":1080,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4NGkyMzY5MUUyQkVBMjVGNDQ3?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4NGkyMzY5MUUyQkVBMjVGNDQ3?revision=12","title":"thynguyen_3-1664297181656.png","associationType":"BODY","width":394,"height":377,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4NmkyNUJFM0M2RTE3RjkzQzZF?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4NmkyNUJFM0M2RTE3RjkzQzZF?revision=12","title":"thynguyen_4-1664297181657.png","associationType":"BODY","width":1429,"height":741,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4OGlERDg2QjlEQTI3MkM3QkM2?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4OGlERDg2QjlEQTI3MkM3QkM2?revision=12","title":"thynguyen_5-1664297181658.png","associationType":"BODY","width":1430,"height":886,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4N2kyNDY1MUE5OUQyM0RDN0Iz?revision=12\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNTM1MTMzLTQwODc4N2kyNDY1MUE5OUQyM0RDN0Iz?revision=12","title":"thynguyen_6-1664297181659.png","associationType":"BODY","width":1316,"height":876,"altText":null},"BlogTopicMessage:message:3535133":{"__typename":"BlogTopicMessage","subject":"Azure Synapse Analytics September Update 2022","conversation":{"__ref":"Conversation:conversation:3535133"},"id":"message:3535133","revisionNum":12,"uid":3535133,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n   \n Welcome to the September 2022 update for Azure Synapse Analytics! This month, you will find information about MERGE for dedicated SQL pools, Auto-statistics for CSV datasets in serverless SQL and the new Livy errors codes for Apache Spark in Azure Synapse Analytics. Additional general updates and new features in Synapse Data Explorer and Data Integration are also mentioned in this edition.   \n   ","introduction":"","metrics":{"__typename":"MessageMetrics","views":9209},"postTime":"2022-09-28T08:00:00.042-07:00","lastPublishTime":"2022-09-28T08:12:20.369-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics September Update 2022  \n Welcome to the September 2022 update for Azure Synapse Analytics! This month, you will find information about MERGE for dedicated SQL pools, Auto-statistics for CSV datasets in serverless SQL and the new Livy errors codes for Apache Spark in Azure Synapse Analytics. Additional general updates and new features in Synapse Data Explorer and Data Integration are also mentioned in this edition.   \n   \n Don’t forget to check out our companion video on the Azure Synapse Analytics YouTube channel! \n   \n   \n   \n Table of contents \n \n SQL  \n \n Auto-statistics for OPENROWSET in CSV datasets  \n MERGE T-SQL is now Generally Available  \n \n \n Apache Spark for Synapse  \n \n New informative Livy errors codes  \n \n \n Synapse Data Explorer  \n \n Logstash connector HTTP/HTTPS proxy configuration  \n Kafka Connect support of Protobuf format  \n Embed Azure Data Explorer dashboards in third-party apps  \n Funnel visuals  \n .NET and Node.js support in Sample App Generator  \n \n \n Data Integration  \n \n Gantt Chart view supported in Integration Runtime Monitoring  \n Monitoring Improvements  \n Maximum column optimization in mapping dataflow  \n Support NUMBER to integer conversion in Oracle source connector   \n Support for sending a body with HTTP request DELETE method in Web activity  \n \n \n \n \n   \n SQL  \n Auto-statistics for OPENROWSET in CSV datasets  \n Serverless SQL pool relies on statistics to generate an optimal query execution plan and resource allocation. Previously, you had to manually create statistics for your CSV datasets when querying with OPENROWSET to get optimal performance due to the lack of CSV auto-statistics feature. With this improvement, serverless SQL pool will automatically create statistics for CSV datasets when needed to ensure an optimal query execution plan is generated. This feature is currently enabled for OPENROWSET only.  \n   \n How much performance improvement CSV statistics will bring depends on query complexity and the amount of data processed. The following chart compares durations of TPCH queries on top of 10MB and 1TB CSV datasets with and without statistics:     \n   \n \n   \n In both cases, improvement is ~25% with no additional user effort. With this improvement, statistics will be created automatically for your CSV datasets queried with OPENROWSET. That way you can enjoy faster queries over CSV files!   \n To learn more, please read Statistics in serverless SQL pool.  \n   \n MERGE T-SQL is now Generally Available  \n We are happy to announce that the MERGE T-SQL command for Azure Synapse Dedicated SQL pools is now Generally Available! MERGE has been a highly requested addition to the Synapse T-SQL library that encapsulates INSERTs/UPDATEs/DELETEs into a single statement, drastically easing migrations and making table synchronization a breeze.  \n   \n \n   \n To ensure you are using the official GA-supported version of MERGE, check that the ‘@@version’ of your Synapse Dedicated SQL pool is on ‘10.0.17829.0’ or beyond. To learn more about the MERGE statement in Synapse SQL Dedicated pools, check out MERGE (Transact-SQL) and the Tech Community Blogpost.  \n   \n Apache Spark for Synapse  \n New informative Livy errors codes  \n Many factors can play into the failure of a Spark application in Azure Synapse Analytics. For example, it can be due to a system error or a user related error. Previously, all errors in failing Spark jobs surfaced with a generic error code displaying LIVY_JOB_STATE_DEAD. This error code gave no further insight into why the job had failed and required significant effort to identify the root cause by digging into the driver, executor, Spark Event and Livy logs to find a solution.  \n   \n To make troubleshooting errors easier, we have introduced a more precise list of error codes that describes the cause of failure and replaces the previous generic error codes. When a Spark job fails on Azure Synapse, the error handling feature now parses and checks the logs on the backend to identify the root cause and displays it on the monitoring pane along with the steps to take to resolve the issue.  \n   \n Error codes are now divided into four different categories:  \n \n User: Indicating a user error  \n System: Indicating a system error  \n Ambiguous: Could be either user or system error  \n Unknown: No classification yet, most probably because the error type isn't included in the model  \n \n Note that this feature is currently disabled by default but will be turned on by default for all Spark application shortly.    For more details, please read Interpret error codes in Synapse Analytics  \n   \n Synapse Data Explorer  \n Logstash connector HTTP/HTTPS proxy configuration  \n Logstash is an open-source server-side data processing pipeline that ingests data from a multitude of sources, transforms it, and then sends it to your favorite destination.  \n   \n The Azure Data Explorer (ADX) Logstash plugin enables you to process events from Logstash into an Azure Data Explorer database for later analysis. The latest release, v1.0.5, now supports HTTP and HTTPS proxies specified through configuration.  \n   \n Kafka Connect support of Protobuf format  \n Using Protocol buffers is increasingly becoming a data exchange choice in IoT based systems because of the excellent schema support, extensibility to various platforms and compression. The ADX Kafka sink connector leverages the Kafka Connect framework and provides an adapter to ingest data from Kafka in JSON, Avro, String and now Protobuf format in the latest update.  \n   \n To learn more, please read Ingesting Protobuf data from Kafka to Azure Data Explorer  \n   \n Embed Azure Data Explorer dashboards in third-party apps  \n Azure Data Explorer dashboards can now be embedded in 3rd party apps. This comes on top of allowing embedding of the Monaco editor in 3rd party apps.  \n   \n Dashboard embedding allows you to easily share data with your customers in a way that allows them to interact and explore it. Using various feature flags, you can manage what controls will be part of the embedded dashboard experience. For example, you can decide to remove sharing, and add connection menu items.   \n   \n To learn more about dashboard embedding in Data Explorer, read Embed dashboards.  \n   \n Funnel visuals  \n Funnel is the latest visual we added to Azure Data Explorer dashboards following the feedback we received from customers. Funnel is usually used to track sequential flows to analyze attrition from stage to stage.  \n \n   \n For more details, read Customize Dashboard Visuals - Funnel  \n   \n .NET and Node.js support in Sample App Generator  \n The sample app generator wizard is a tool that allows you to create a working app to ingest and query your data in your preferred programming language. You can use this generated code as a baseline to write your own apps, altering the code as you go, or you can copy sections of the code into your own apps. The code includes comments with tips, best practices, links to reference material, and recommended TODO changes when adapting the code to your needs.   \n   \n Starting today, generating sample apps in .NET and Node.js is supported along with the previously available options – Java & Python. Give it a try here.  \n   \n To learn more, please read Use the sample app generator wizard to create code to ingest and query your data.  \n    \n Data Integration  \n Gantt Chart view supported in Integration Runtime Monitoring  \n You now have the option to view your activity runs with a Gantt chart in Integration Runtime monitoring. A Gantt chart is a view that allows you to see the run history over a time range. This will give you additional monitoring views of your integration runtimes.   \n   \n \n   \n For more information, read Monitor integration runtime in Azure Data Factory.  \n   \n Monitoring Improvements  \n We’ve released a new bundle of improvements to the monitoring experience based on community feedback.   \n   \n Sorting: We now have more columns available for sorting. Previously, you could only sort by Run start. Now, you can sort by Pipeline Name, Run End, and Status. In addition to that, you can also sort by Trigger Name and Status when viewing triggers.   \n   \n Pipeline Consumption Report: The pipeline consumption report now shows a loading icon when there are many activities in a pipeline to let you know when the consumption information has been fully calculated.   \n   \n Rerun Pipeline Grouping: Previously, when pipelines were re-run, all re-runs were retrieved for the monitoring UI, even when the re-runs were not viewed. Now, pipeline groupings for re-runs will only fetch that data when the grouping is expanded. This will improve the performance of monitoring pages that have multiple pipeline re-run groupings.   \n   \n Time zone settings: Monitoring time zone settings are now saved in your browser cache with your configured columns so that you don’t have to reconfigure your settings each time.   \n   \n Pipeline runs: When monitoring pipeline runs, all records for pipelines with many activities will be retrieved. We’ve now added pages so that you can navigate through all the activity records.  \n   \n To learn more about these updates, read Azure Data Factory monitoring improvements.   \n   \n Maximum column optimization in mapping dataflow  \n For CSV data sources, we’ve added a new setting that allows you to set the maximum number of columns. The default is 20480 so if you have more than 20480 columns, you can customize this value and optimize it for your dataset.   \n   \n \n   \n To learn more about this setting, refer to this document: Delimited text format in Azure Data Factory.  \n   \n Support NUMBER to integer conversion in Oracle source connector   \n We have added new support to convert Oracle NUMBER type to corresponding integer in source. You will see a new property convertDecimalToInteger, which will allow you to convert Oracle NUMBER type from zero or an unspecified scale to the corresponding integer.   \n   \n To learn more about this update, read Copy data to and from Oracle by using Azure Data Factory or Azure Synapse Analytics.  \n   \n Support for sending a body with HTTP request DELETE method in Web activity  \n \n We have added support for sending a body (optional) when using the DELETE method in Web activity. This is useful in the case where your web endpoint expects a body to be passed with the HTTP request DELETE method.  \n \n   \n To learn more, refer to Web Activity in Azure Data Factory and Azure Synapse Analytics.   \n   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Synapse Analytics August Update 2022","conversation":{"__ref":"Conversation:conversation:3535126"},"id":"message:3535126","revisionNum":18,"uid":3535126,"depth":0,"board":{"__ref":"Blog:board:AzureSynapseAnalyticsBlog"},"author":{"__ref":"User:user:889224"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n Welcome to the August 2022 update for Azure Synapse Analytics! This month, you will find information about Distribution Advisor for dedicated SQL pools, Spark Delta Lake tables in serverless SQL and the new Cast transformation that was added to mapping data flows. Additional general updates and new features in Dedicated and Serverless SQL, Spark, Synapse Data Explorer, Data Integration, Machine Learning and Security are also mentioned in this edition.  ","introduction":"","metrics":{"__typename":"MessageMetrics","views":13189},"postTime":"2022-08-30T08:18:18.873-07:00","lastPublishTime":"2022-08-30T12:26:51.342-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n Azure Synapse Analytics August Update 2022 \n Welcome to the August 2022 update for Azure Synapse Analytics! This month, you will find information about Distribution Advisor for dedicated SQL pools, Spark Delta Lake tables in serverless SQL and the new Cast transformation that was added to mapping data flows. Additional general updates and new features in Dedicated and Serverless SQL, Spark, Synapse Data Explorer, Data Integration, Machine Learning and Security are also mentioned in this edition.  \n \n   \n Don’t forget to check out our companion video on the Azure Synapse Analytics YouTube channel! \n   \n \n   \n Note: we stopped the companion video auto-play for this month's blog, comment below if this is a monthly feature you want us to continue as part of the blog. \n   \n Table of contents \n \n SQL \n \n Add SQL objects and users in Lake databases \n Spark Delta Lake tables are available in serverless SQL pool for public preview \n Multi-column Distribution is available for public preview in dedicated SQL pools \n Distribution Advisor is available for public preview in dedicated SQL pools \n Accelerate your Oracle data warehouse migration to dedicated SQL pools in Azure Synapse Analytics \n \n \n Apache Spark for Synapse \n \n Azure Synapse Analytics Spark elastic pool storage is available for public preview \n Azure Synapse Analytics Spark Optimized Write is available for public preview \n \n \n Synapse Data Explorer \n \n Free cluster upgrade option \n Analyzing fresh ADX data from Excel pivot table \n ADX Web Explorer- Query results exploration - Color by value \n ADX Web Explorer- Crosshair support for charts (ysplit=panels) \n Data mapping transformations \n \n \n Data Integration \n \n Mapping data flows now support visual Cast transformation \n Change default activity time out from 7 days to 12 hours \n Expression builder UI update \n New UI for mapping dataflows inline datasets \n \n \n Machine Learning \n \n .NET, C#, and F# Support \n OpenAI language models \n Full support for MLflow \n No cluster, no problem: Experiment in browser with Binder \n Other updates \n \n \n Security \n \n Execute Azure Synapse Spark Notebooks with system-assigned managed identity \n \n \n \n   \n SQL \n Add SQL objects and users in Lake databases \n Lake databases allow you to use SQL to do analytics on top of data in the data link. We're always trying to make lake databases better and bring them closer to a traditional SQL database. \n   \n Starting this month, you can perform these operations in the Lake database: \n \n Create schemas where you can place SQL views, procedures, and inline table value functions (iTVF) \n Create views, procedures, and inline table-valued functions\n \n Note that the dbo schema is still reserved for tables defined using Apache Spark pools, Synapse Database Designer, and Dataverse \n \n \n Create database users from your Azure Active Directory domain and assign them to the db_datareader role. This way you can create database-scoped users who can read data only within your Lake database. \n \n   \n With these improvements, the T-SQL functionalities in Lake databases are closer than ever to the SQL database experience you are familiar with. \n   \n To learn more, read Access lake databases using serverless SQL pool in Azure Synapse Analytics and Create and use native external tables using SQL pools in Azure Synapse Analytics. \n   \n Spark Delta Lake tables are available in serverless SQL pool for public preview \n Serverless SQL pools enable you to query Parquet and CSV tables that are created using Spark notebooks. \n   \n With the latest version of Synapse, serverless SQL pools now allows you to access Delta Lake tables created in Spark databases. When you create a table using the Delta Lake format in Apache Spark pools in Synapse workspace, the serverless SQL pool will create a matching table and enable you to access your Delta Lake files using the T-SQL language. This feature is currently in public preview. \n   \n To learn more, read Azure Synapse Analytics shared metadata tables. \n   \n Multi-column Distribution is available for public preview in dedicated SQL pools \n 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. Multi-column Distribution (MCD) in public preview will allow you to choose up to 8 columns for distribution.   \n   \n To participate in the preview, you can opt into the new database compatibility level 9000.  For example: \n   \n ALTER DATABASE SCOPED CONFIGURATION SET \n   \n At any given time, you can run this command to opt-out of the public preview.  Please note that this will disable the MCD feature and access to existing MCD tables.  To re-gain preview access, you can opt into compatibility level 9000 again. \n   \n ALTER DATABASE SCOPED CONFIGURATION SET DW_COMPATIBILITY_LEVEL = AUTO; \n   \n To learn more, read the following Microsoft docs: \n \n ALTER DATABASE SCOPED CONFIGURATION - SQL Server (Transact-SQL)CREATE TABLE AS SELECT (Azure Synapse Analytics) - SQL Server \n CREATE TABLE (Azure Synapse Analytics) - SQL Server \n CREATE MATERIALIZED VIEW AS SELECT (Transact-SQL) - SQL Server \n sys.pdw_column_distribution_properties (Transact-SQL) - SQL Server \n \n   \n Distribution Advisor is available for public preview in dedicated SQL pools \n In Azure Synapse SQL, you can customize the way each table is distributed. Table distribution strategy affects query performance substantially. \n   \n Distribution Advisor (DA) is a new feature in Synapse Dedicated SQL Gen2 that analyzes queries and recommends the best distribution strategies for tables to improve query performance. Queries to be considered by the advisor can be provided by you or pulled from your historic queries available in DMV (Dynamic Management Views). \n   \n To learn more, read Distribution Advisor in Azure Synapse SQL. \n   \n Accelerate your Oracle data warehouse migration to dedicated SQL pools in Azure Synapse Analytics \n Modernizing your on-premise data warehouse by migrating to Azure Synapse reduces maintenance costs, greatly improves performance, and provides high availability. However, migrating from on-premise to a Cloud data warehouse can be complex and time consuming. Extract, transform and load (ETL) processes, large amounts of data and reports built over the years need to be migrated while navigating differences in architecture and design, database objects and data types, performance tuning, ETL and SQL. \n   \n We are happy to introduce the new Oracle migration guide to help you resolve common issues and accelerate your migration of Oracle data warehouses to dedicated SQL pools in Azure Synapse Analytics. This guide provides migration steps, process and guidelines, and insights on the capabilities of SQL Server Migration Assistant (SSMA) for Oracle to automate migration tasks. \n   \n Take advantage of our migration guides to modernize your data warehouse and accelerate your business by integrating with Azure Synapse. \n   \n To learn more about Oracle, Teradata and IBM Netezza migrations, read Azure Synapse Analytics migration guides \n   \n Apache Spark for Synapse \n Azure Synapse Analytics Spark elastic pool storage is available for public preview \n Azure Synapse Analytics Spark pools now support elastic pool storage. Apache Spark in Azure Synapse Analytics utilizes temporary VM disk storage while the Spark pool is instantiated. Spark jobs write shuffle map outputs, shuffle data and spilled data to local VM disks. Examples of operations that may utilize local disk are sort, cache, and persist. \n   \n In the past, when temporary VM disk space ran out, Spark jobs failed due to “Out of Disk Space” error (java.io.IOException: No space left on device).  In the case of “Out of Disk Space” errors, much of the burden to prevent jobs from failing shifts to you to reconfigure the Spark jobs (e.g., tweak the number of partitions) or clusters (e.g., add more nodes to the cluster). These errors might not be consistent, and you may end up experimenting heavily by running production jobs. This process could be expensive in multiple dimensions: \n \n Wasted time – Customers are required to experiment heavily with job configurations via trial and error and are expected to understand Spark’s internal metrics to make the correct decision. \n Wasted resources – Production jobs can process varying amounts of data, Spark jobs can fail non-deterministically if resources are not over-provisioned. For instance, consider the problem of data skew, which may result in a few nodes requiring more disk space than others. Currently in Synapse, each node in a cluster gets the same disk space size and increasing disk space across all nodes is not an ideal solution and leads to tremendous waste. \n Slowdown in job execution – In the hypothetical scenario where we solve the problem by autoscaling nodes (assuming costs are not an issue to the customer), adding a compute node is expensive (takes a few minutes) as opposed to adding storage (takes a few seconds). \n \n   \n Starting this month, 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 less job failures as a result. \n   \n Azure Synapse Analytics Spark Optimized Write is available for public preview \n Analytical workloads on Apache Spark perform most efficiently when using standardized larger file sizes. The relation between the file size, the number of files, the number of Spark workers and its configurations play a fundamental role with performance. Ingestion workloads into data lake tables may have the inherited characteristic of constantly writing lots of small files; this scenario is commonly known as the \"small file problem\". \n   \n Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written by Apache Spark and aims to increase individual file size of the written data. It dynamically optimizes partitions while generating files with an optimal size. This feature achieves the file size by using an extra data shuffle phase over partitions, causing an extra processing cost while writing the data. The small write penalty should be outweighed by read efficiency on the tables. \n   \n The Optimize Write feature is now in Public and readily available on Synapse runtimes for Apache Spark 3 (3.1 and 3.2). \n   \n To learn more about the usage scenarios and how to enable the feature, read The need for optimize write on Apache Spark \n   \n Synapse Data Explorer \n Free cluster upgrade option \n You can now upgrade your Azure Data Explorer free cluster to a full cluster that removes the storage limitation allowing you more capacity to grow your data.  \n   \n An Azure Data Explorer full cluster has the following additional benefits than the free cluster: \n \n Native to Azure \n Enterprise ready \n Unlimited storage (beyond the free 100GB) \n Unlimited scalability with auto scale feature that automatically turns on / off compute resources based on actual usage \n End-to-end solution for data ingestion, query, visualization, and management \n Integration to Azure Monitor and Insights \n Event Hub and Event Grid connectors \n \n Upgrading is easy and it is done by associating your free cluster and its data to an Azure subscription. From the ADX Web Explorer, go to the menu item My Cluster (Preview) and select the Upgrade to Azure Cluster button, fill in some required details, and you are on your way to an upgraded cluster! \n   \n \n   \n To learn more, read Upgrade a free Azure Data Explorer cluster. \n Analyzing fresh ADX data from Excel pivot table \n Through Power BI (PBI) datasets, you can now query Kusto data in real time in Excel without having to import any data and without any volume limitations. You can build the PBI model using DQ to Kusto, publish to the PBI service and connect Excel to the published dataset. \n   \n MDX queries generated by the Pivot code, will find their way to the Kusto backend as KQL statements that will aggregate the data as needed by the pivot and back to Excel. \n   \n To learn more, read Use fresh and unlimited volume of ADX data (Kusto) from your favorite analytic tool - Excel pivot. \n   \n ADX Web Explorer- Query results exploration - Color by value \n When working with large datasets, being able to highlight unique data at-a-glance can be valuable as you can visually group rows that share identical values for a specific column. \n   \n To perform this, right-click a value in the results grid, and choose Explore results –> Color by value and the rows will be colored based on the selected column. \n   \n \n   \n \n Note: By using the “Color by value” again on the same column will cancel coloring, using it on a different column will re-color the rows based on a different column. \n   \n ADX Web Explorer- Crosshair support for charts (ysplit=panels) \n The ysplit property now supports the crosshair visual (vertical lines that move along the mouse pointer), when using the render operator for the following charts: \n \n Bar \n Column \n Time \n Area \n \n   \n The crosshair visual provides an easier correlation of the data between multiple graphs and can be used in place of tooltips. \n   \n \n   \n Data mapping transformations \n Parquet, JSON and AVRO, are some of the data format mappings that support simple and useful ingestion-time transformations such as converting DateTime from Unix seconds, transforming JSON property bag array to a valid JSON document or adding source artifact location. \n   \n As part of the ingestion flow, you can now create mapping transformations on the table column by selecting the relevant transformation. \n   \n \n   \n These transformations are super helpful for quick transformations. Where the scenario requires more complex processing at ingestion time, use Update policy, which allows defining lightweight processing using KQL expression. \n   \n To learn more, read Data mapping transformations. \n   \n Data Integration \n Mapping data flows now support visual Cast transformation \n It is very common to transform data types into data integration workloads. An example of that is to convert strings to dates. Previously, you had to use Expression Builder and remember the correct syntax or keywords to convert data types in mapping data flows. \n   \n For example, the syntax to convert a string ‘Jul-01-2022’ to date 2022-07-01 is toDate(‘Jul-01-2022’, ‘MMM-dd-yyyy’, ‘en-US’).’ \n   \n Starting this month, you can use the cast transformation to quickly transform data types. \n   \n \n   \n To learn more about mapping dataflows, read Cast transformation in mapping data flow. \n   \n Change default activity time out from 7 days to 12 hours \n We heard your feedback, and it was clear that the default pipeline activity timeout of 7 days was too long! While you can still customize the timeout, the default activity time out for Azure Data Factory and Azure Synapse Analytics is now 12 hours. \n   \n \n   \n To learn more about this change, read Azure Data Factory Changing Default Pipeline Activity. \n   \n Expression builder UI update \n We’ve updated our expression builder UI to make pipeline designing easier. Our pipeline expression builder used to list out all components in a single pane. \n   \n \n   \n Now, to make it easier to work with different pipeline components, we’ve created new content category tabs to make it easier to find what you’re looking for. \n   \n \n   \n To learn more, read Coming soon to ADF: More Pipeline Expression Builder Ease-of-Use Enhancements! \n   \n New UI for mapping dataflows inline datasets \n We’ve updated our data flow source UI to make it easier to find your inline dataset type. Previously, you would have to scroll through the list or filter to find your inline dataset type. We now have categories that group your dataset types, making it easier to find what you’re looking for. \n   \n \n To learn more, read Mapping data flows in Azure Data Factory. \n   \n Machine Learning \n \n A new version of the SynapseML library (previously MMLSpark) is now available on Azure Synapse. SynapseML is an open-source library that aims to simplify the creation of massively scalable machine learning pipelines. The new version, v0.10.0, includes .NET support, OpenAI language models, MLFlow integration and much more. Continue reading for details about all the highlights. \n   \n .NET, C#, and F# Support \n In SynapseML v0.10, we are adding full support for .NET languages like C# and F#. This means that you can now use everything in SynapseML from any of the .NET ecosystem languages, and even load up models you built in other languages like Python, R, and Java. \n   \n Here is an example of training a LightGBM model using .NET: \n \n   \n To learn more, read Announcing SynapseML for .NET – Large Scale ML with a Simple API. \n To learn more with a .NET example, read .NET Example with LightGBMClassifier. \n   \n OpenAI language models \n SynapseML also offers a simple and scalable way to leverage Azure Cognitive Services directly from Spark. Currently, SynapseML supports over 50 Cognitive Services and we are now expanding that with the new Azure Open AI Service. This service allows users to tap into 175-Billion parameter language models (GPT-3) from OpenAI that can generate and complete text and code near human parity. \n   \n To learn more, read the SynapseML OpenAI guide. \n To learn more with a simple demo, view Create Custom Search Engines from Unstructured Data with SynapseML. \n   \n Full support for MLflow \n MLflow is a platform for managing the machine learning lifecycle and streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. We are very happy to announce that SynapseML models now integrates with MLflow with full support for saving, loading, deployment, and autologging! \n   \n To learn more, read MLflow in SynapseML getting started guide and SynapseML Autologging. \n   \n No cluster, no problem: Experiment in browser with Binder \n We know that Spark can be intimidating for first users but fear not because with the technology Binder, you can explore and experiment with SynapseML with zero setup, install, infrastructure, or Azure account required. Simply visit the SynapseML Binder site to get started in your browser! \n   \n Other updates \n In addition to the news mentioned above, there are many other great updates in this release for Responsible AI, Azure Cognitive Services, LightGBM on Spark, Wowpal Wabbit and other features. We could not capture all of it here but you can read more about this release in the detailed SynapseML v0.10.0 release notes and learn all about our rich capabilities on the SynapseML website. \n   \n Security \n Execute Azure Synapse Spark Notebooks with system-assigned managed identity \n Spark notebooks execute with the users’ Azure Active Directory identity, whereas pipelines execute with the system-assigned managed identity. In the past, if a pipeline with notebook activities failed, it used to be difficult to recreate or debug the issue. There was no easy way to execute a notebook with the system-assigned managed identity. \n   \n Starting this month, you can easily execute Spark Notebooks with the system assigned managed identity (or workspace managed identity) by enabling Run as managed identity from the Configure session menu. \n   \n With this feature, you will be able to validate that your notebook works as expected when using the system-assigned managed identity, before using the notebook in a pipeline. \n   \n To learn more, read Managed Identity for Azure Synapse. \n   \n To execute Spark Notebooks with system-assigned managed identity, you will need to have the following RBAC roles: \n \n Synapse Compute Operator on the workspace or selected Spark pool \n Synapse Credential User on the system-assigned managed identity \n \n \n   \n \n   \n \n   \n   \n   \n   \n   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