https://cloudblogs.microsoft.com/sqlserver/2019/09/10/the-september-release-of-azure-data-studio-is-now-available/Source: https://cloudblogs.microsoft.com/sqlserver/2019/09/10/the-september-release-of-azure-data-studio-is-now-available/ Today were announcing the September 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 Azure Data READ MORE
Today were announcing the support in Visual Studio Code for SQL Server 2019 Big Data Clusters PySpark development and query submission. It provides complementary capabilities to Azure Data Studio for data engineers to author and productionize PySpark jobs after data scientists data explore and experimentation. The Visual Studio Code Apache Spark and Hive extension enables you to enjoy cross platform and enhanced light weight Python editing capabilities. It covers scenarios around Python authoring, debugging, Jupyter Notebook integration, and notebook like interactive query.
With the Visual Studio Code extension, you can enjoy native Python programming experiences such as linting, debugging support, language service, and so on. You can run current line, run selected lines of code, or run all for your PY file. You can import and export a .ipynb notebook and perform a notebook like query including Run Cell, Run Above, or Run Below. You can also enjoy a notebook like interactive experience that includes your source code and markdown comments along with the running results and output. You can remove the unneeded sections, enter comments, or type additional code in the interactive results window. Moreover, you can visualize your results in a graphic format through a matplotlib like Jupyter Notebook.
In continuation with our announcement of SQL Server 2019 release candidate last week, were announcing that the release candidate refresh forSQL Server 2019 is now available to download. The release candidate now includes bits for Big Data Clusters in SQL Server 2019 in this refresh.
Back in July, we announced the preview of Big Data Clusters in SQL Server 2019 and since then weve seen our customers actively bringing their big data analytical workloads to SQL Server 2019 to operationalize their AI and machine learning projects.
“Building and deploying our vertical AI-solution for clinical radiology combines very diverse implementation paradigms, data formats, and regulatory requirements. SQL Server 2019 Big Data Clusters allowed us to accommodate and integrate all aspects from one shared platform – for our data scientists with their deep learning as well as for our software engineers who wire up workflows, security, and scalability. At runtime, our healthcare customers benefit from simple containerized deployment and maintenance while being able to move our solution between on-prem and the cloud easily.” – Ren Balzano, Founder and CEO of Balzano
Big Data Clusters in SQL Server 2019 gives you the ability to build new big data applications in SQL Server or move your existing
Today were announcing the availability of the first public release candidate for SQL Server 2019, which is now available for download. SQL Server 2019 brings the industry-leading performance and security of SQL Server to Windows, Linux, and containers and can tackle any data workload from business intelligence to data warehousing to analytics and AI over all your data both structured and unstructured.
In our nine community technology previews (CTPs) to date, SQL Server 2019 has delivered:
1) Intelligence over all of your data with SQL Server 2019 Big Data Clusters
Your business is not constrained by the type of data that gets in the database. Now with SQL Server 2019, you can do analytics and AI over any type of data, structured, or unstructured with the power SQL and Apache Spark. You can enhance your high-value structured data by combining it with big data and the ability to dynamically scale-out compute to support analytics over the Hadoop Distributed File System (HDFS) at scale.
Data virtualization allows you to have a single query point where you run your T SQL code or connect your BI tools to, to join your disparate data and fetch the results. No more data movement, just
Today were announcing the August release of Azure Data Studio is now available.
Please note: After downloading Azure Data Studio, click Yes to enabling preview features so that you can use the extensions.
Azure Data Studio is a multi-database, cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. To learn more, please visit our GitHub.
The key highlights to cover this month include:
SandDance integrationA new way to interact with dataNotebook improvementsSQL Server Dacpac extension can support Azure Active DirectorySQL Server 2019 extensionVisual Studio Code merge 1.37Bug fixes
For a complete list of updates, please refer to the release notes.
SandDance integrationA new way to interact with data
To enhance customers data visualization and exploration experience within Azure Data Studio, we’re pleased to announce the new feature, visualizer. This feature is currently powered by SandDance, a data visualization tool developed by Microsoft Research.
Smart chart detection:
SandDance 1.30 is now supporting the smart chart detection feature. This feature will recommend a suitable default chart configuration including chart type, aces, and color based on the data set.
SandDance supports three-dimension visualization that brings your data set to life.
Today were excited to announce our latest Microsoft Machine Learning Server 9.4 release, which addresses popular customer requests as well as developments in the R and Python community.
Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features and algorithmic innovation that brings the best of open source and proprietary worlds together.
This release is a continuation of the R and Python value that you have come to see as part of Microsoft R and previous releases of Microsoft Machine Learning Server. Machine Learning Server supports the full data science lifecycle of your R and Python-based analytics. Additionally, Machine Learning Server enables operationalization support so you can deploy your models to a scalable grid for both batch and real-time scoring.
Whats new in Machine Learning Server 9.4
Weve released Microsoft R Open 3.5.2 and 3.5.3 across various supported platforms. Please find more about Microsoft R Open on the Microsoft R Application
Support for SQL Server 2008 and 2008 R2 ended on July 9, 2019. That means the end of regular security updates. However, if you move those SQL Server instances to Azure or Azure Stack, Microsoft will give you three years of extended security updates at no additional cost. If youre currently running SQL Server 2008 or 2008 R2 and you are unable to update to a later version of SQL Server, you will want to take advantage of this offer rather than running the risk of facing a future security vulnerability. An unpatched instance of SQL Server could lead to data loss, downtime, or a devastating data breach.
If you have a SQL Server failover cluster instance (FCI) or use hypervisor-based high availability, or other clustering technology on-premises for high availability, youll probably need the same in Azure. If you need to migrate to Azure/Azure Stack at end of support, and you require high availability for SQL Server 2008/2008 R2, theres only one solution recommended by Microsoft: SIOS DataKeeper. SIOS DataKeeper enables clustering in the cloud, including the creation of a SQL Server 2008/2008 R2 failover cluster instance, allowing you to achieve your high availability goals.
In order to mitigate the risk
We are excited to announce the release of SQL Server Management Studio (SSMS) 18.2. For this update, while we added some features, our focus was dedicated to fundamentals such as stability, reliability, performance, etc.
You can download SQL Server Management Studio 18.2 today.
Some of the new features in SQL Server Management Studio include:
Intellisense/editor: Added support for data classificationQuery execution: Added a completion time in the messages to track when a given query completed its execution.ShowPlan: Added new attribute in query plan when the inline scalar UDF feature is enabled.Key bug fixes in this release include:SQL Server Management Studio setupFixed or greatly mitigated an issue where SQL Server Management Studio setup was incorrectly blocking the installation of SQL Server Management Studio reporting mismatching languages. This could have been an issue in some abnormal situations like an aborted setup or an incorrect uninstall of a previous version of SQL Server Management Studio. You can review the feedback on this issue to learn more.Always OnFixed an issue where SQL Server Management Studio was throwing an error when trying to delete an availability group that has a single quote in its name.Fixed an issue where SQL Server Management Studio was presenting the wrong failover wizard
Java is one of the most important open source projects in the world today. Born nearly 25 years ago around the same time as Microsoft SQL Server, it has since grown to a community of millions of developers around the world and by many reports is the most popular programming language in the world.
SQL Server has a long history with Java and that relationship has been deepening even more lately. It all started more than 20 years ago with the early versions of the Java database connectivity (JDBC) driver to enable Java applications to connect to SQL Server.
In SQL Server 2016, we introduced the first version of PolyBase, the data virtualization solution enabling customers to query data in Cloudera or Hortonworks from SQL Server using T-SQL that relies heavily on Java for the interop layer.
Now, in Big Data Clusters for SQL Server 2019, with the inclusion of Apache Spark, HDFS, and other big data components in the box, SQL Server and Java have become even more intertwined.
Historically, SQL Server has allowed customers to choose to bring their own Java runtime, typically OpenJDK or Oracles Java SE. Neither option was ideal though. With OpenJDK, there wasnt a support vendor if you
Were excited to announce the monthly release of SQL Server 2019 community technology preview (CTP) 3.2.
With this release of SQL Server 2019 community technology preview 3.2, we are announcing the public preview of Big Data Clusters for SQL Server 2019. Big Data Clusters for SQL Server enables big data analytics within SQL Server. It brings HDFS and Apache Spark into SQL Server for scale out compute and storage.
Big data clusters allow you to deploy scalable clusters of SQL Server, Apache Spark, and HDFS running on Kubernetes. It provides all the tools and systems to ingest, store, and prepare data for analysis as well as to train and operationalize machine learning models. It allows you to query external data sources through data virtualization and combine and analyze your high-value relational data with high-volume big data. You will be also be able to build and deploy scalable and productive data-driven applications in big data clusters.
Our early adopter customers are already using Big Data Clusters for SQL Server 2019 for their production workloads. Check out what they have to say below:
Systems Imagination Inc.
With SQL Server 2019 big data clusters, we can solve for on-demand big data experiments. We can analyze cancer research data