Our customers have asked for this and weve been listening advanced data security is now available for SQL Server onAzure Virtual Machines!Using just a few simple steps, you can nowprotectyour SQL ServerinstallationsonAzure VMswith Microsofts advanceddatasecurity capabilities.
Advanced data security for SQL Server on Azure VM currently includes functionality for surfacing and mitigating potential database vulnerabilities and detecting anomalous activities that could indicate a threat to your server. To get started today, read the Advanced data security for SQL Server onVMsetup instructions.
Why you should enable advanced data security for SQL Server on Azure VM
While in public preview, advanced data security for SQL Server on Azure VM is free and includes:
Vulnerability assessment – A database scanning service that can discover, track, and help you remediate potential database vulnerabilities. Detected vulnerabilities across all connected SQL Servers will appear in one unified dashboard!
Advanced threat protection – A detection service that continuously monitors your database for suspicious activities and provides action-oriented security alerts on anomalous database access patterns. All alerts will appear in your centralized go-to location for security management in the Azure portal the Azure Security Center threatsdashboard.
These advanced securityfeatureshaveevolved andbenefited from continuous improvementover the past couple of years,and havealreadybeenrunning on more than 1 million databases inthecorrespondingAzureSQL DatabaseserviceAdvanced datasecurityforAzure SQLdatabases.
How does it work?
UsingtheAzureLog Analytics agent,you connect your SQL Servers hosting machine to a Log Analytics workspace. The agent collects audit logsforlogin events (omitting anysensitivedatalike queries or users data) and uploadsthem from the machine to the workspace,whereour security analytics capabilitiesgointo action.In addition, the agent alsocollects results from the vulnerability assessment scansand sendsthose to the workspace as well.
Logsand assessment resultswill appear in the workspace andareentirelyunderyourcontrol andcan be queriedfor more insights. You can alsoidentifythe logs that triggered Advanced Threat Protection alertsfor further investigation.Finally,the workspacecontainsa built-in dashboard forintuitive analysis ofthevulnerabilityassessment results.
IoT is ushering in an exciting—and sometimes exasperating—time of innovation. Adoption isn’t easy, so it’s important to hold a vision of the promise of Industry 4.0 in mind as you get ready for this next wave of business.
IoT can serve as an onramp to continual transformation, providing companies with the ability to capitalize more fully on automation, AI, and machine learning. As companies harness the power of IoT, cloud services, robotics, and other emerging technologies, they’ll discover new ways of working, creating, and living. They’ll test and learn more swiftly, and scale results in the most promising areas. And this innovation will find form in smart buildings, more efficient factories, connected cities, fully autonomous vehicles, a healthier environment, and better lives.
Between now and that digital world, there are years of trial and error and dozens of applications ahead. But companies across the spectrum are embedding IoT to attain data and analytics mastery, optimize processes, create new services, and rethink products right now. Their leaders are positioning themselves and their companies to take advantage of the promise of digitization across industries.
IoT innovation is not one size fits all. What it means for a process manufacturing firm is necessarily different than what it will mean for a healthcare company. To help you understand how you might apply IoT to your business—and learn from companies that have gone before you—here are four different innovation plays.
Push service optimization to new levels
With almost all companies competing on the customer experience, it makes sense to optimize service levels to trim cost, error, and delay from customer-facing processes. Better service can be a key differentiator in the marketplace. And when it’s paired with continual optimization enabled by IoT, your customers start seeing the benefit in their businesses.
Jabil is one of the world’s largest and most innovative providers of manufacturing, design engineering, and supply-chain-management technologies. Jabil was quick to recognize that keeping and increasing its competitive edge required the company to accelerate production cycles and personalize products. Its customers might order a product only once, meaning that they couldn’t afford the time delays and waste of traditional inspection processes. “We have many products that customers expect to [have] in their shops within a week,” says Matt Behringer, chief information officer for enterprise operations and quality systems at Jabil. “And that is including transit.”
Jabil used an IoT approach based on the Microsoft Azure Cortana Intelligence Suite to connect systems, gain predictive intelligence, and increase its flexibility and scalability. In a pilot project that connected an electronics manufacturing production line to the cloud, Jabil was able to anticipate and avoid more than half of circuit board failures at the second step in the process, and the remaining 45 percent at the sixth step. By using AI and machine learning, Jabil can correct board errors even earlier in the process, reducing scrapped materials, product failures, and warranty issues. Now, the IoT platform monitors all individual production lines and collects data from every Jabil factory and product worldwide. Jabil is pushing optimization further by using deep neural networks to refine its automated optical inspection process, increasing speed and accuracy to new levels.
“One of the things we’re able to do with predictive analytics in Azure is reduce waste, whether it’s from a process or design issue, or as a result of maintaining enough excess inventory to ensure we have enough for shipment. We’re confident we can produce a good-quality product all the way through the line,” says Behringer.
Leverage data from a digital ecosystem
As companies build IoT-enabled systems of intelligence, they’re creating ecosystems where partners work together seamlessly in a fluid and ever-changing digital supply chain. Participants gain access to a centralized view of real-time data they can use to fine-tune processes, and analytics to enable predictive decision-making. In addition, automation can help customers reduce sources of waste such as unnecessary resource use.
PCL Construction comprises a group of independent construction companies that perform work in the United States, the Caribbean, and Australia. Recognizing that smart buildings are the future of construction, PCL is partnering with Microsoft to drive smart building innovation and focus implementation efforts.
The company is using the full range of Azure solutions—Power BI, Azure IoT, advanced analytics, and AI—to develop smart building solutions for multiple use cases, including increasing construction efficiency and workplace safety, improving building efficiency by turning off power and heat in unused rooms, analyzing room utilization to create a more comfortable and productive work environment, and collecting usage information from multiple systems to optimize services at an enterprise level. PCL’s customers benefit with greater control, more efficient buildings, and lower energy consumption and costs.
However, the path forward wasn’t easy. “Cultural transformation was a necessary and a driving factor in PCL’s IoT journey. To drive product, P&L, and a change in approach to partnering, we had to first embrace this change as a leadership team,” says PCL manager of advanced technology services Chris Palmer.
Develop a managed-services business
Essen, Germany-based thyssenkrupp Elevator is one of the world’s leading providers of elevators, escalators, and other passenger transportation solutions. The company uses a wide range of Azure services to improve usage of its solutions and streamline maintenance at customers’ sites around the globe.
With business partner Willow, thyssenkrupp has used the Azure Digital Twin platform to create a virtual replica of its Innovation Test Tower, an 800-foot-tall test laboratory in Rottweill, Germany. The lab is also an active commercial building, with nearly 200,000 square feet of occupied space and IoT sensors that transmit data 24 hours a day. Willow and thyssenkrupp are using IoT to gain new insights into building operations and how space is used to refine products and services.
In addition, thyssenkrupp has developed MAX, a solution built on the Azure platform that uses IoT, AI, and machine learning to help service more than 120,000 elevators worldwide. Using MAX, building operators can reduce elevator downtime by half and cut the average length of service calls by up to four times, while improving user satisfaction.
The company’s MULTI system uses IoT and AI to make better decisions about where elevators go, providing faster travel times or even scheduling elevator arrival to align with routine passenger arrivals.
“We constantly reconfigure the space to test different usage scenarios and see what works best for the people in the [Innovation Test Tower] building. We don’t have to install massive new physical assets for testing because we do it all through the digital replica—with keystrokes rather than sledgehammers. We have this flexibility thanks to Willow Twin and its Azure infrastructure,” says professor Michael Cesarz, chief executive officer for MULTI at thyssenkrupp.
Rethink products and services for the digital era
Kohler, a leading manufacturer, is embedding IoT in its products to create smart kitchens and bathrooms, meeting consumer demand for personalization, convenience, and control. Built with the Microsoft Azure IoT platform, the platform responds to voice commands, hand motions, weather, and consumer preset options.
And Kohler innovated fast, using Azure to demo, develop, test, and scale the new solutions. “From zero to demo in two months is incredible. We easily cut our development cycle in half by using Azure platform services while also significantly lowering our startup investment,” says Fei Shen, associate director of IoT engineering at Kohler.
The smart bathroom and kitchen products can start a user’s shower, adjust the water temperature to a predetermined level, turn on mirror lights to preferred brightness and color, and share the day’s weather and traffic. They also warn users if water floods their kitchen and bathroom. The smart fixtures provide Kohler with critical insights into how consumers are using their products, which they can use to develop new products and fine-tune existing features.
Kohler is betting that consumer adoption of smart home technology will grow and is pivoting its business to meet new demand. “We’ve been making intelligent products for about 10 years, things like digital faucets and showers, but none have had IoT capability. We want to help people live more graciously, and digitally enabling our products is the next step in doing that,” said Jane Yun, Associate Marketing Manager in Smart Kitchens and Baths at Kohler.
As these examples show, the possibilities for IoT are boundless and success is different for every company. Some firms will leverage IoT only for internal processes, while others will use analytics and automation to empower all the partners in their digital ecosystems. Some companies will wrap data services around physical product offerings to optimize the customer experience and deepen relationships, while still others will rethink their products and services to tap emerging market demand and out-position competitors.
How will you apply IoT insights to transform your businesses and processes? Get help crafting your IoT strategy and maximizing your opportunities for ROI.
This is the first blog post dedicated to Analysis Services on the Power BI blog. As discussed in this post on the old Analysis Services team blog, Microsoft is moving off MSDN and TechNet blogs. We have made it clear that Power BI will be a one-stop shop for both enterprise and self-service BI on …
During Microsoft Build we announced the preview of the visual interface for Azure Machine Learning service. This new drag-and-drop workflow capability in Azure Machine Learning service simplifies the process of building, testing, and deploying machine learning models for customers who prefer a visual experience to a coding experience. This capability brings the familiarity of what we already provide in our popular Azure Machine Learning Studio with significant improvements to ease the user experience.
The Azure Machine Learning visual interface is designed for simplicity and productivity. The drag-and-drop experience is tailored for:
Data scientists who are more familiar with visual tools than coding.
Users who are new to machine learning and want to learn it in an intuitive way.
Machine learning experts who are interested in rapid prototyping.
It offers a rich set of modules covering data preparation, feature engineering, training algorithms, and model evaluation. Another great aspect of this new capability is that it is completely web-based with no software installation required. All of this to say, users of all experience levels can now view and work on their data in a more consumable and easy-to-use manner.
One of the biggest challenges data scientists previously faced when training data sets was the cumbersome limitations to scaling. If you were to start by training on a smaller model and then had a need to expand it due to an influx of data, or complex algorithms you would be required to migrate your entire data set to continue your training. With the new visual interface for Azure Machine Learning we’ve replaced the back end to reduce these limitations.
An experiment authored in the drag-and-drop experience can run on any Azure Machine Learning Compute cluster. As your training scales up on larger data sets or complex models, the Azure Machine Learning compute can autoscale from single node to multi node each time an experiment is submitted to run. With autoscaling you can now start with small models and not worry about expanding your production work with bigger data. By removing scaling limitations, data scientists now can focus on their training work.
Deploying a trained model to a production environment previously required knowledge of coding, model management, container service, and web service testing. We wanted to provide an easier solution to this challenge so that these skills are no longer necessary. With the new visual interface, customers of all experience levels can deploy a trained model with just a few clicks. We will discuss how to launch this interface later in this blog.
Once a model is deployed, you can test the web service immediately from this new user visual interface. Now you can test to make sure your models are correctly deployed. All web service inputs are now pre-populated for convenience. The web service API and sample code are also automatically generated. These procedures normally used to take hours to perform, but now with the new visual interface it can all happen within just a few clicks.
Full integration of Azure Machine Learning service
As the newest capability of Azure Machine Learning service, the visual interface brings the best of Azure Machine Learning service and Machine Learning Studio together. The assets created in this new visual interface experience can be used and managed in the Azure Machine Learning service workspace. These include experiments, compute, models, images, and deployments. It also natively inherits the capabilities like run history, versioning, and security of Azure Machine Learning service.
How to use
See for yourself just how easy it is to use this interface with just a few clicks. To access this new capability, open your Azure Machine Learning workspace in the Azure portal. In your workspace, select visual interface (preview) to launch the visual interface.
Were excited to announce the monthly release of SQL Server 2019 community technology preview (CTP) 3.0. For customers in the Early Adoption Program, CTP 3.0 is the first release where you’re able to run SQL Server 2019 in production. To apply, please reach out to your assigned Program Manager for more information. Check out the What’s new in SQL Server 2019 preview documentation to learn more.
CTP 3.0 preview brings the following new features and capabilities to SQL Server 2019:
Big data clusters
Scale out by supporting deployment configurations with an increased number of SQL Server instances in the compute pool. You can now specify up to 4 instances in the compute pool for optimal performance of your queries against data pool, storage pool, or other external data sources.
The mssqlctl utility includes updates to ease the big data cluster management experience with enhancements to the login experience. There is also a new command to list the cluster endpoints.
Persistent volumes abstract the details of how the storage is provided and how its consumed. In this release, were enhancing the supported storage configurations by enabling you to customize storage classes independently for logs and data. With these changes, we also consolidated the storage configurations for big data components, so that the number of persistent volume claims for a big data cluster has been reduced for a default minimum configuration cluster.
The new Java language software development kit (SDK) for SQL Server to simplify development of Java programs is now open sourced and available on GitHub.
Database administrators can register new external language extensions on any OS platform supported by SQL Server. This enables developers to create language extensions for additional languages such as dotnet core, Go, and more.
Master Data Services
Master Data Services (MDS) enables you to manage your organizations reference and master data. You can organize the data into models, create rules for updating the data, and control who updates the data. In CTP 3.0, you can host the MDS database on SQL Azure Managed Instance and can run MDS over SQL Azure-hosted data without having to host another SQL Server for MDS.
Added new query language extensions for the analysis services engine, allowing for better globalization support.
Ready to learn more?
If you want to run SQL Server 2019 CTP 3.0 in production with full support from Microsoft support and youre already enrolled in the Early Adoption Program (EAP), please contact your assigned Program Manager to get set up for production support.
You’re responsible for overseeing the transportation of a pallet of medicine halfway around the world. Drugs will travel from your pharmaceutical company’s manufacturing outbound warehouse in central New Jersey to third-party logistics firms, distributors, pharmacies, and ultimately, patients. Each box in that pallet – no bigger than the box that holds the business cards on your desk – contains very costly medicine, the product of 10 years of research and R&D spending.
Oh, and there’s a catch – actually several. You will need to ensure compliance with a long list of requirements from temperature and vibration to whether the box has been opened. The box must be kept at a stable temperature of between 2-8 degrees Celsius the whole journey. Additionally, the box is as vulnerable to shock as a Faberge egg. And the contents of each box can easily be faked. And another catch: your company isn’t in the global logistics business, and you lose oversight of those boxes of precious medicine as soon as they leave your freight bay in New Jersey.
IoT opens a new era for secure, smart cold chain asset management
It used to be that the only solution available for you to monitor and manage your cold chain was for your freight technicians to toss a data logger in the center of each outbound pallet and hope for the best. The shipment was passed from the third-party logistics firm to distributors, to warehouses, past freight forwarders, onto last-mile distribution, and finally on to the pharmacy and patients. Your visibility was minimal while your exposure to drug waste or potential counterfeiting was high.
Microsoft and Wipro envisioned a better solution. One that that would help ensure the cold chain was maintained from production to delivery to customers. And one that would limit issues like counterfeiting.
“Azure IoT technology enabled us to develop a real-time IoT solution that provided the alerts and analytics needed to maintain the cold chain and decrease counterfeiting costs for pharmaceutical customers,” explained Sujan Thanjavuru, Head of Life Sciences Strategy & Transformation, Wipro, Ltd. “We worked with our customer to customize the sensors and develop a user interface that made it easy for managers to understand the state of their pharma shipments in real time. The result was an easy-to-use dashboard that provided valuable insights.”
“Azure IoT brings greater efficiency and reliability to customer value chains with world-class IoT and location intelligence services,” added Tony Shakib, IoT Business Acceleration Leader, Microsoft Azure.
Imagine a future with reduced counterfeit drugs and cold chain product wastage
Fast forward: imagine you’ve implemented Titan Secure from Wipro. Now, your outbound freight technician slaps a small, flexible bluetooth low energy (BLE) beacon sensor onto each box of medication, which is paired with the FDA and EMA-compliant serial number and barcode. The sensors measure temperature, humidity, shock, vibration, and tamper data. They generate geospatial alerts in real time in the event of a temperature excursion or potential counterfeiting attempts. The information is stored in and displayed from Azure. Data is transferred on the backend using Microsoft blockchain, but shipping operators don’t need to know what that means to use it. On an easy-to-use, interactive map and dashboard, technicians can easily track each individual box of your company’s product as it’s shipped from your outbound warehouse all the way to the pharmacy. Your managers receive an alert when a shipment is predicted to get too hot, so that you can call the third party and fix the problem before the shipment has to be destroyed. Once you notice tampering within one of your shipments, you’ll find out quickly what’s happened and how many boxes have been affected.
Manage your cold chain in real-time
What does this mean for your company? Wipro’s Thanjavuru explained, “Pharmaceutical companies can now digitally transform their cold chain management. They can monitor temperature and telemetry data through the entire product journey, view analytics and alerts within the Titan Secure dashboard for visibility including anti-counterfeiting support, and – with cloud connectivity – information about the shipment is available in near real-time.”
We’re happy to announce our May 2019 update of Power BI Report Server! This release has many reporting features including conditional formatting for titles and other visual objects, the performance analyzer pane, accessibility support for visuals. The new modeling view is also featured in this release. Read on to learn more about the new features …
Dozens of Power BI visuals come out-of-the box with Power BI Desktop. These visuals are available in the visualizations’ pane when you create or edit Power BI content. These visuals are just the beginning of the available options to help you tell your data story. Learn about the variety of options you get with Power BI visuals and how to manage them in your organization.