Source: https://blogs.msdn.microsoft.com/azuredatalake/2017/11/18/getting-new-insights-into-your-usage-of-data-lake-analytics/ Users of Azure Data Lake Analytics consistently ask for more insights about their usage for both monitoring jobs and also to understand how much they are spending on big data. Today, we are announcing some key updates READ MORE
Source: https://powerbi.microsoft.com/en-us/blog/power-bi-expands-access-to-intelligence-for-external-guest-users/ Power BI was first introduced with a simple commitment: Empower people and organizations with access to critical intelligence. The recent general availability of Power BI Premium in June broadened…
This post was authored by Bob Ward, Principal Architect, and Jamie Reding, Senior Program Manager and Performance Architect, Microsoft Database Systems Group.
SQL Server continues to be a proven leader in database performance for both analytic and OLTP workloads. SQL Server 2017 is fast, built-in with capabilities and features such as Columnstore indexes to accelerate analytic performance and Automatic Tuning and Adaptive Query Processing to keep your database application at peak speed.
Recently, Hewlett Packard Enterprise (HPE) announced a new world record TPC-H 10TB benchmark result¹ using SQL Server 2017 and Windows Server 2016 on their new DL580 Gen10 Server. This new amazing result at 1,479,748 Composite Query-per-Hour (QphH) was achieved at price/performance of .95 USD per QphH continuing to show SQL Server’s leadership in price and performance.
HPE also announced the first TPC-H 3TB result² on a 2-socket system using SQL Server 2017 and Windows Server 2016 with their DL380 Gen Server. They achieved a stellar 1,014,374 QphH on only 2-sockets. These results continue to show how powerful SQL Server can be to handle your analytic query workloads including data warehouses.
SQL Server also is a proven leader for OLTP workloads. Lenovo recently announced a new world-record TPC-E benchmark
This post is authored by Barnam Bora, Program Manager in the Cloud AI group at Microsoft.
Microsoft’s Data Science Virtual Machines (DSVM) and Deep Learning Virtual Machines (DLVM) are a family of popular VM images in Windows Server and Linux flavors that are published on the Azure Marketplace. They have a curated but broad set of pre-configured machine learning and data science tools including pre-loaded samples. DSVM and DLVM are configured and tested to work seamlessly with a plethora of services available on the Microsoft Azure cloud, and they enable a wide array of data analytics scenarios that are being used by many organizations across the globe.
We recently hosted a webinar covering the workflow of building ML and AI -powered solutions in Azure using DSVM, DLVM and related services such as Azure Batch AI and Azure Machine Learning Model Management. The webinar video is available from the link below (requires registration with Microsoft) and more information about the webinar are in the sections that follow.
Scenarios Covered in the Webinar
Single GPU Node AI Model Training
DSVM and DLVM are great tools to develop, test and deploy AI models and solutions. Data scientists
This post is by Corom Thompson, Principal Software Engineer at Microsoft.
On November 22nd, 1963, the President of the United States, John F. Kennedy, was assassinated. He was shot by a lone gunman named Lee Harvey Oswald while driving through the streets of Dallas in his motorcade. The assassination has been the subject of so much controversy that, 25 years ago, an act of Congress mandated that all documents related to the assassination be released this year. The first batch of released files has more than 6,000 documents totaling 34,000 pages, and the last drop of files contains at least twice as many documents.
We’re all curious to know what’s inside them, but it would take decades to read through these. We approached this problem of gaining insights by using Azure Search and Cognitive Services to extract knowledge from this deluge of documents, using a continuous process that ingests raw documents, enriching them into structured information that enables you to explore the underlying data.
Today, at the Microsoft Connect(); 2017 event, we created the demo web site* shown in Figure 1 below – this is a web application that uses the AzSearch.js library and designed to give you interesting insights into this vast trove of information.
Figure 1 – JFK Files web application for exploring the
Re-posted from the Azure blog.
We made some exciting AI-related announcements at Microsoft Connect(); 2017 earlier today. Specifically, we talked about how we’re making it even easier for developers and data scientists to infuse AI into new and existing apps with these new capabilities:
Azure IoT Edge integration in Azure ML.
A new Azure Databricks service that combines the best of Databricks and Azure for Spark-based analytics.
A new Visual Studio Tools for AI development environment with Azure ML integration.
With these updates, the Microsoft AI platform – summarized in the picture below – now offers comprehensive cloud-based, on-premises, and edge support – in other words, all the infrastructure, tools, frameworks, services and solutions needed by developers, data scientists and businesses to infuse AI into their products and services.
Check out the original post here to learn about these updates in more detail, and about the innovative ways in which our customers and putting these new AI technologies to use in the real world.
ML Blog Team
This post is authored by Ted Way, Senior Program Manager at Microsoft.
Today we are excited to announce the ability to bring intelligence to the edge with the integration of Azure Machine Learning and Azure IoT Edge. Businesses today understand how artificial intelligence (AI) and machine learning (ML) are critical to help them go from telling the “what happened” story to the “what will happen” and “how can we make it happen” story. The challenge is how to apply AI and ML to data that cannot make it to the cloud, for instance due to data sovereignty, privacy, bandwidth or other issues. With this integration, all models created using Azure Machine Learning can now be deployed to any IoT gateways and devices with the Azure IoT Edge runtime. These models are deployed to the edge in the form of containers and can run on very small footprint devices.
There many use cases for the intelligent edge, where a model is trained in the cloud and then deployed to an edge device. For example, a hospital wants to use AI to identify lung cancer on CT scans. Due to patient privacy and bandwidth limitations, a large CT
We are excited to announce that SQL Operations Studio is now available in preview. SQL Operations Studio is a free, light-weight tool for modern database development and operations for SQL Server on Windows, Linux and Docker, Azure SQL Database and Azure SQL Data Warehouse on Windows, Mac or Linux machines.
Download SQL Operations Studio to get started.
It’s easy to connect to Microsoft SQL Server with SQL Operations Studio and perform routine database operations—overall lowering the learning curve for non-professional database administrators who have responsibility for maintaining their organization’s SQL-based data assets.
As more organizations adopt DevOps for application lifecycle management, developers and other non-professional database administrators find themselves taking responsibility for developing and operating databases. These individuals often do not have time to learn the intricacies of their database environment, making hard to perform even the most routine tasks. Microsoft SQL Operations Studio takes a prescriptive approach to performing routine tasks, allowing users to get tasks done fast while continuing to learn on the job.
Users can leverage their favorite command line tools (e.g. Bash, PowerShell, sqlcmd, bcp and ssh) in the integrated terminal window right within the SQL Operations Studio user interface. They can easily generate
Data is everywhere today: in the cloud, on premises, and everywhere in between, tied up in systems of nearly endless complexity. Microsoft solutions allow developers to innovate while also scaling and growing their data infrastructure. In SQL Server 2016 SP1, SQL Server made available a consistent programmable surface layer for all its editions, making it easy to write applications that target any edition of the database. This year’s release takes it a step further with native support for Linux and Docker.
Microsoft puts the needs of the developer front and center in its data solutions. We have created the most advanced set of tools to radically lower the barriers to getting data – of any type, from anywhere – into the application design and build process. Today with the preview of Microsoft SQL Operations Studio, you can now access, run and manage this data from the system of your choice, on Windows, Linux and Docker.
Committed to choice for both database platform and tools
SQL Server 2017 also makes it easier to drive innovation via a CI/CD pipeline with the support of Docker containers. Since the Community Technology Preview of SQL Server 2017, there have been over 2 million Docker
Source: https://blogs.microsoft.com/iot/2017/11/15/azure-iot-edge-brings-ai-advanced-analytics-capabilities-edge/ Remote locations. Rugged job sites. Spotty connectivity. In many industries, such conditions are a reality, making Internet of Things scenarios such as cloud analytics and real-time response more costly and unpredictable. Today Microsoft announced the public READ MORE