Were excited to announce the monthly release of SQL Server 2019 community technology preview (CTP) 2.5. SQL Server 2019 is the first release of SQL Server to closely integrate Apache Spark and the Hadoop Distributed File System (HDFS) with SQL Server in a unified data platform.
The CTP 2.5 preview brings the following new features and capabilities to SQL Server 2019:
Big data clusters For more control and flexibility over the big data cluster layout and configuration settings, were introducing a new deployment mechanism that uses configuration files to deploy your cluster. You can start from the built-in configurations that come with the mssqlctl utility and customize them to accommodate the platform you want to run the big data cluster on. To streamline the deployment process, mssqlctl utility enables an interactive deployment experience that guides you through the steps to initiate the deployment with prompts for required inputs. You can also automate the entire process using mssqlctl configuration commands available to list, customize, or deploy using configuration files. To avoid incompatibilities between client utility and big data cluster server versions, you can now verify you installed the right version of the utility by using mssqlctl –version command. The new
https://cloudblogs.microsoft.com/sqlserver/2019/04/18/the-april-release-of-azure-data-studio-is-now-available/Source: https://cloudblogs.microsoft.com/sqlserver/2019/04/18/the-april-release-of-azure-data-studio-is-now-available/ We are excited to announce the April release of Azure Data Studio (formerly known as SQL Operations Studio) is now available. Download Azure Data Studioand review the Release notes to get started. Please note: After downloading READ MORE
This post was co-authored by Jeff Shepherd, Deepak Mukunthu, and Vijay Aski.
Recently, we blogged about performing automated machine learning on SQL Server 2019 big data clusters. In todays post, we will present a complementary automated machine learning approach leveraging Azure Machine Learning service (Azure ML) invoked from SQL Server. While the previous post dealt with a Spark-based implementation tuned for big data, this post presents an approach that runs directly in SQL Server running on a single server. This is well suited for use with data residing in SQL Server tables and provides an ideal solution for any version of SQL Server that supports SQL Server Machine Learning Services.
Azure Machine Learning service
Azure Machine Learning service is a cloud service. We call the service from SQL Server to manage and direct the automated training of machine learning models in SQL Server. Automated machine learning tries a variety of machine learning pipelines. It chooses the pipelines using its own machine learning model based on the scores from previous pipelines. Automated machine learning can be used from SQL Server Machine Learning Services, python environments such as Jupyter notebooks and Azure notebooks, Azure Databricks, and Power BI.
Starting in SQL Server
Businesses today are faced with the challenge of staying profitable and investing in future innovations. Speed and flexibility are the name of the game, and effective data-scaling techniques are highly sought after. Yet exploding data volumes, diverse data types, and numerous database management systems make it harder than ever for data professionals to consolidate data and synthesize key business insights.
SQL Server 2019 big data clusters simplify security, deployment, and management of all of your key data workloads and data lakes, while including innovative security and compliance features, industry-leading performance, and mission-critical availability of the platform.
In this webinar I will join Travis Wright, Principal Program Manager at Microsoft to demonstrate how to simplify big data to make faster and better business decisions with the new SQL Server 2019 big data clusters feature. Learn more about how the latest edition has evolved beyond your grandfather’s SQL Server to a unified data platform that includes distribution for Hadoop, Apache Spark, and AI. Attend ready to learn about:
Using data virtualization, integrate, query, and retrieve all of your data from relational, non-relational, or unstructured data sourcesincluding big datawithout replicating or moving the data. Easily manage all of this data with a
Were delighted to release the Azure Toolkit for IntelliJ support for SQL Server Big Data Cluster Spark job development and submission. For first-time Spark developers, it can often be hard to get started and build their first application, with long and tedious development cycles in the integrated development environment (IDE). This toolkit empowers new users to get started with Spark in just a few minutes. Experienced Spark developers also find it faster and easier to iterate their development cycle.
The toolkit extends IntelliJ support for the Spark job life cycle starting from creation, authoring, and debugging, through submission of jobs to SQL Server Big DataClusters. Itenables you to enjoy a native Scala and Java Spark application development experience and quickly start a project using built-in templates and sample code. The integration with SQL Server Big Data Cluster empowers you to quickly submit a job to the big data cluster as well as monitor its progress. The Spark console allows you to check schemas, preview data, and validate your code logic in a shell-like environment while you can develop Spark batch jobs within the same toolkit.
The Azure Toolkit for IntelliJ offers the following capabilities:
Connect to SQL Server Big
https://blogs.msdn.microsoft.com/sqlsecurity/2019/03/29/we-have-moved/Source: https://blogs.msdn.microsoft.com/sqlsecurity/2019/03/29/we-have-moved/ Thanks for visiting! This blog has now been migrated to: https://techcommunity.microsoft.com/t5/Azure-SQL-Database/bg-p/Azure-SQL-Database/label-name/SQLServerSecurity
https://blogs.msdn.microsoft.com/sql_server_team/we-have-moved/Source: https://blogs.msdn.microsoft.com/sql_server_team/we-have-moved/ Thanks for visiting! This blog has now been migrated to: https://techcommunity.microsoft.com/t5/SQL-Server/bg-p/SQLServer/label-name/SQLServerTiger
As we get closer to the General Availability of SQL Server Management Studio (SSMS) 18, we have decided to have a quick release of the Release Candidate (RC) build.
This SQL Server Management Studio 18 RC1 build has some important updates as seen below:
SSMS improvements: Enabling XMLA endpoint connectivity to Power BI datasets: XMLA endpoints provide access to the Analysis Services engine in the Power BI Service. This allows tools such as SSMS and SQL Profiler to connect to Power BI datasets for monitoring, management, and debugging etc. For more details, please review XMLA endpoint connectivity in the Power BI blog. SQL Server Management Objects (SMO) Added cascade delete support to “Edge Constraints” in both SMO and SSMS. Added support for data classification “read-write” permissions. Audit Files Updated the list of known audit actions to include FEATURE RESTRICTION ADD/CHANGE GROUP/DROP
In addition, we also have several bug fixes in the following areas:
SSMS: Fixed an issue which was preventing MFA authentication when user ids belonged to multiple tenants. Fixed an issue where the
Continuing with our monthly release cadence, were excited to announce the release of SQL Server 2019 community technology Preview 2.4. Previewed in September 2018, SQL Server 2019 is the first release of SQL Server to closely integrate Apache Spark and the Hadoop Distributed File System (HDFS) with SQL Server in a unified data platform. Watch the introductory video below to learn more.
Watch the video The CTP 2.4 preview brings the following new features and capabilities to SQL Server 2019: Big data clusters Introducing the use of powerful GPUs for running deep learning workloads using industry standard TensorFlow libraries in Spark. The Spark runtime engine has been upgraded to Spark 2.4, which provides a new scheduler that works better with MPI workloads , new high-order functions that enable complex data types, as well as many SparkSQL and Pandas improvements. Database engine New diagnostics have been added for actual execution plans through extended events and a Dynamic Management Function (DMF) that leverages lightweight Query profiling. This preview also introduces transparent data encryption (TDE) scan with suspend and resume syntax so that you can pause the scan while the workload on the system
As a database administrator (DBA), Ive always known what the role involved. Developers swing by my desk requesting a new database, a data refresh, or help with challenges theyre facing. They sometimes get frustrated with me when their database or data isnt ready, even if its really the server admin who hasnt allocated the storage or provisioned the new server yet. My manager might come by and ask what Im doing and, after I try to describe the technical process Im working on, walk away with a puzzled look on his face. When others ask what I do all day, the developers respond that DBA stands for Dont Bother Asking.
This was my life, but it wasnt always satisfying. It was days of backups, database refreshes, after-hours database outages, and weekend patching. Once, this was what gave me value and my day meaning until one day I asked myself, Is this all there is? I had years of database administration, automation, and scripting experience. I was highly detail-oriented and cared about the success of my team. What was next?
Is all that daily slog the best use of your skills?
In a word, cloud. Maybe you too have