Kategorie: Microsoft Azure

What’s new in Azure IoT Central – March 2019

In IoT Central, our aim is to simplify IoT. We want to make sure your IoT data drives meaningful actions and visualizations. In this post, I will share new features now available in Azure IoT Central including embedded Microsoft Flow, updates to the Azure IoT Central connector, Azure Monitor action groups, multiple dashboards, and localization support. We also recently expanded Jobs functionality in IoT Central, so you can check out the announcement blog post to learn more.

Microsoft Flow is now embedded in IoT Central

You can now build workflows using your favorite connectors directly within IoT Central. For example, you can build a temperature alert rule that triggers a workflow to send push notifications and SMS all in one place within IoT Central. You can also test and share the workflow, see the run history, and manage all workflows attached to that rule.

Try it out in your IoT Central app by visiting Device Templates in Rules, adding a new action, and picking the Microsoft Flow tile.

Updated Azure IoT Central connector: Send a command and get device actions

With the updated Azure IoT Central connector, you can now build workflows in Microsoft Flow and Azure Logic Apps that can send commands on an IoT device and get device information like the name, properties, and settings values. You can also now build a workflow to tell an IoT device to reboot from a mobile app, and display the device’s temperature setting and location property in a mobile app.

Try it out in Microsoft Flow or Azure Logic Apps by using the Send a command and Get device actions in your workflow.

Integration with Azure Monitor action groups

Azure Monitor action groups are reusable groups of actions that can be attached to multiple rules at once. Instead of creating separate actions for each rule and entering in the recipient’s email address, SMS number, and webhook URL for each, you can choose an action group that contains all three from a drop down and expect to receive notifications on all three channels. The same action group can be attached to multiple rules and are reusable across Azure Monitor alerts.

Try it out in your IoT Central app by visiting Device Templates in Rules, adding a new action, and then pick the Azure Monitor action groups tile.

Multiple dashboards

Users can now create multiple personal dashboards in their IoT Central app! You can now build customized dashboards to better organize your devices and data. The default application dashboard is still available for all users, but each user of the app can create personalized dashboards and switch between them.

Localization support

As of today, IoT Central supports 17 languages! You can select your preferred language in the settings section in the top navigation, and this will apply when you use any app in IoT Central. Each user can have their own preferred language, and you can change it at any time.

With these new features, you can more conveniently build workflows as actions and reuse groups of actions, organize your visualizations across multiple dashboards, and work with IoT Central with your favorite language. Stay tuned for more developments in IoT Central. Until next time!

Next steps

Have ideas or suggestions for new features? Post it on Uservoice.
To explore the full set of features and capabilities and start your free trial, visit the IoT Central website.
Check out our documentation including tutorials to connect your first device.
To give us feedback about your experience with Azure IoT Central, take this survey.
To learn more about the Azure IoT portfolio including the latest news, visit the Microsoft Azure IoT page.

Quelle: Azure

Announcing Azure Stack HCI: A new member of the Azure Stack family

It has been inspiring to watch how customers use Azure Stack to innovate and drive digital transformation across cloud boundaries. In her blog post today, Julia White shares examples of how customers are using Azure Stack to innovate on-premises using Azure services. Azure Stack shipped in 2017, and it is the only solution in the market today for customers to run cloud applications using consistent IaaS and PaaS services across public cloud, on-premises, and in disconnected environments. While customers love the fact that they can run cloud applications on-premises with Azure Stack, we understand that most customers also run important parts of their organization on traditional virtualized applications. Now we have a new option to deliver cloud efficiency and innovation for these workloads as well.

Today, I am pleased to announce Azure Stack HCI solutions are available for customers who want to run virtualized applications on modern hyperconverged infrastructure (HCI) to lower costs and improve performance. Azure Stack HCI solutions feature the same software-defined compute, storage, and networking software as Azure Stack, and can integrate with Azure for hybrid capabilities such as cloud-based backup, site recovery, monitoring, and more.

Adopting hybrid cloud is a journey and it is important to have a strategy that takes into account different workloads, skillsets, and tools. Microsoft is the only leading cloud vendor that delivers a comprehensive set of hybrid cloud solutions, so customers can use the right tool for the job without compromise. 

Choose the right option for each workload

Azure Stack HCI: Use existing skills, gain hyperconverged efficiency, and connect to Azure

Azure Stack HCI solutions are designed to run virtualized applications on-premises in a familiar way, with simplified access to Azure for hybrid cloud scenarios. This is a perfect solution for IT to leverage existing skills to run virtualized applications on new hyperconverged infrastructure while taking advantage of cloud services and building cloud skills.

Customers that deploy Azure Stack HCI solutions get amazing price/performance with Hyper-V and Storage Spaces Direct running on the most current industry-standard x86 hardware. Azure Stack HCI solutions include support for the latest hardware technologies like NVMe drives, persistent memory, and remote-direct memory access (RDMA) networking.

IT admins can also use Windows Admin Center for simplified integration with Azure hybrid services to seamlessly connect to Azure for:

Azure Site Recovery for high availability and disaster recovery as a service (DRaaS).
Azure Monitor, a centralized hub to track what’s happening across your applications, network, and infrastructure – with advanced analytics powered by AI.
Cloud Witness, to use Azure as the lightweight tie breaker for cluster quorum.
Azure Backup for offsite data protection and to protect against ransomware.
Azure Update Management for update assessment and update deployments for Windows VMs running in Azure and on-premises.
Azure Network Adapter to connect resources on-premises with your VMs in Azure via a point-to-site VPN.
Azure Security Center for threat detection and monitoring for VMs running in Azure and on-premises (coming soon).

Buy from your choice of hardware partners

Azure Stack HCI solutions are available today from 15 partners offering Microsoft-validated hardware systems to ensure optimal performance and reliability. Your preferred Microsoft partner gets you up and running without lengthy design and build time and offers a single point of contact for implementation and support services. 

Visit our website to find more than 70 Azure Stack HCI solutions currently available from these Microsoft partners: ASUS, Axellio, bluechip, DataON, Dell EMC, Fujitsu, HPE, Hitachi, Huawei, Lenovo, NEC, primeLine Solutions, QCT, SecureGUARD, and Supermicro.

Learn more

We know that a great hybrid cloud strategy is one that meets you where you are, delivering cloud benefits to all workloads wherever they reside. Check out these resources to learn more about Azure Stack HCI and our other Microsoft hybrid offerings:

Register for our Hybrid Cloud Virtual Event on March 28, 2019.
Learn more at our Azure Stack HCI solutions website.
Listen to Microsoft experts Jeff Woolsey and Vijay Tewari discuss the new Azure Stack HCI solutions.

FAQ

What do Azure Stack and Azure Stack HCI solutions have in common?

Azure Stack HCI solutions feature the same Hyper-V based software-defined compute, storage, and networking technologies as Azure Stack. Both offerings meet rigorous testing and validation criteria to ensure reliability and compatibility with the underlying hardware platform.

How are they different?

With Azure Stack, you can run Azure IaaS and PaaS services on-premises to consistently build and run cloud applications anywhere.

Azure Stack HCI is a better solution to run virtualized workloads in a familiar way – but with hyperconverged efficiency – and connect to Azure for hybrid scenarios such as cloud backup, cloud-based monitoring, etc.

Why is Microsoft bringing its HCI offering to the Azure Stack family?

Microsoft’s hyperconverged technology is already the foundation of Azure Stack.

Many Microsoft customers have complex IT environments and our goal is to provide solutions that meet them where they are with the right technology for the right business need. Azure Stack HCI is an evolution of Windows Server Software-Defined (WSSD) solutions previously available from our hardware partners. We brought it into the Azure Stack family because we have started to offer new options to connect seamlessly with Azure for infrastructure management services.

Will I be able to upgrade from Azure Stack HCI to Azure Stack?

No, but customers can migrate their workloads from Azure Stack HCI to Azure Stack or Azure.

How do I buy Azure Stack HCI solutions?

Follow these steps:

Buy a Microsoft-validated hardware system from your preferred hardware partner.
Install Windows Server 2019 Datacenter edition and Windows Admin Center for management and the ability to connect to Azure for cloud services
Option to use your Azure account to attach management and security services to your workloads.

How does the cost of Azure Stack HCI compare to Azure Stack?

This depends on many factors.

Azure Stack is sold as a fully integrated system including services and support. It can be purchased as a system you manage, or as a fully managed service from our partners. In addition to the base system, the Azure services that run on Azure Stack or Azure are sold on a pay-as-you-use basis.

Azure Stack HCI solutions follow the traditional model. Validated hardware can be purchased from Azure Stack HCI partners and software (Windows Server 2019 Datacenter edition with software-defined datacenter capabilities and Windows Admin Center) can be purchased from various existing channels. For Azure services that you can use with Windows Admin Center, you pay with an Azure subscription.

We recommend working with your Microsoft partner or account team for pricing details.

What is the future roadmap for Azure Stack HCI solutions?

We’re excited to hear customer feedback and will take that into account as we prioritize future investments.
Quelle: Azure

Azure Data Box Family Meets Customers at the Edge

Today I am pleased to announce the general availability of Azure Data Box Edge and the Azure Data Box Gateway. You can get these products today in the Azure Portal.

Compute at the edge

We’ve heard your need to bring Azure compute power closer to you – a trend increasingly referred to as edge computing. Data Box Edge answers that call and is an on-premises anchor point for Azure. Data Box Edge can be racked alongside your existing enterprise hardware or live in non-traditional environments from factory floors to retail aisles. With Data Box Edge, there's no hardware to buy; you sign up and pay-as-you-go just like any other Azure service and the hardware is included.

This 1U rack-mountable appliance from Microsoft brings you the following:

Local Compute – Run containerized applications at your location. Use these to interact with your local systems or to pre-process your data before it transfers to Azure.
Network Storage Gateway – Automatically transfer data between the local appliance and your Azure Storage account. Data Box Edge caches the hottest data locally and speaks file and object protocols to your on-premise applications.
Azure Machine Learning utilizing an Intel Arria 10 FPGA – Use the on-board Field Programmable Gate Array (FPGA) to accelerate inferencing of your data, then transfer it to the cloud to re-train and improve your models. Learn more about the Azure Machine Learning announcement.
Cloud managed – Easily order your device and manage these capabilities for your fleet from the cloud using the Azure Portal.

Since announcing Preview at Ignite 2018 just a few months ago, it has been amazing to see how our customers across different industries are using Data Box Edge to unlock some innovative scenarios:

 

Sunrise Technology, a wholly owned division of The Kroger Co., plans to use Data Box Edge to enhance the Retail as a Service (RaaS) platform for Kroger and the retail industry to enable the features announced at NRF 2019: Retail's Big Show, including personalized, never-before-seen shopping experiences like at-shelf product recommendations, guided shopping and more. The live video analytics on Data Box Edge can help store employees identify and address out-of-stocks quickly and enhance their productivity. Such smart experiences will help retailers provide their customers with more personalized, rewarding experiences.

Esri, a leader in location intelligence, is exploring how Data Box Edge can help those responding to disasters in disconnected environments. Data Box Edge will allow teams in the field to collect imagery captured from the air or ground and turn it into actionable information that provides updated maps. The teams in the field can use updated maps to coordinate response efforts even when completely disconnected from the command center. This is critical in improving the response effectiveness in situations like wildfires and hurricanes.

Data Box Gateway – Hardware not required

Data Box Edge comes with a built-in storage gateway. If you don’t need the Data Box Edge hardware or edge compute, then the Data Box Gateway is also available as a standalone virtual appliance that can be deployed anywhere within your infrastructure.

You can provision it in your hypervisor, using either Hyper-V or VMware, and manage it through the Azure Portal. Server message block (SMB) or network file system (NFS) shares will be set up on your local network. Data landing on these shares will automatically upload to your Azure Storage account, supporting Block Blob, Page Blob, or Azure Files. We’ll handle the network retries and optimize network bandwidth for you. Multiple network interfaces mean the appliance can either sit on your local network or in a DMZ, giving your systems access to Azure Storage without having to open network connections to Azure.

Whether you use the storage gateway inside of Data Box Edge or deploy the Data Box Gateway virtual appliance, the storage gateway capabilities are the same.

 

More solutions from the Data Box family

In addition to Data Box Edge and Data Box Gateway, we also offer three sizes of Data Box for offline data transfer:

Data Box – a ruggedized 100 TB transport appliance
Data Box Disk – a smaller, more nimble transport option with individual 8 TB disks and up to 40 TB per order
Data Box Heavy Preview – a bigger version of Data Box that can scale to 1 PB.

All Data Box offline transport products are available to order through the Azure Portal. We ship them to you and then you fill them up and ship them back to our data center for upload and processing. To make Data Box useful for even more customers, we’re enabling partners to write directly to Data Box with little required change to their software via our new REST API feature which has just reached General Availability – Blob Storage on Data Box!

Get started

Thank you for partnering with us on our journey to bring Azure to the edge. We are excited to see how you use these new products to harness the power of edge computing for your business. Here’s how you can get started:

Order Data Box Edge or the Data Box Gateway today via the Azure Portal.
Review server hardware specs on the Data Box Edge datasheet.
Learn more about our family of Azure Data Box products.

Quelle: Azure

Accelerated AI with Azure Machine Learning service on Azure Data Box Edge

Along with the general availability of Azure Data Box Edge that was announced today, we are announcing the preview of Azure Machine Learning hardware accelerated models on Data Box Edge. The majority of the world’s data in real-world applications is used at the edge. For example, images and videos collected from factories, retail stores, or hospitals are used for manufacturing defect analysis, inventory out-of-stock detection, and diagnostics. Applying machine learning models to the data on Data Box Edge provides lower latency and savings on bandwidth costs, while enabling real-time insights and speed to action for critical business decisions.

Azure Machine Learning service is already a generally available, end-to-end, enterprise-grade, and compliant data science platform. Azure Machine Learning service enables data scientists to simplify and accelerate the building, training, and deployment of machine learning models. All these capabilities are accessed from your favorite Python environment using the latest open-source frameworks, such as PyTorch, TensorFlow, and scikit-learn. These models can run today on CPUs and GPUs, but this preview expands that to field programmable gate arrays (FPGA) on Data Box Edge.

What is in this preview?

This preview enhances Azure Machine Learning service by enabling you to train a TensorFlow model for image classification scenarios, containerize the model in a Docker container, and then deploy the container to a Data Box Edge device with Azure IoT Hub. Today we support ResNet 50, ResNet 152, DenseNet-121, and VGG-16. The model is accelerated by the ONNX runtime on an Intel Arria 10 FPGA that is included with every Data Box Edge.

Why does this matter?

Over the years, AI has been infused in our everyday lives and in industry. Smart home assistants understand what we say, and social media services can tag who’s in the picture we uploaded. Most, if not all, of this is powered by deep neural networks (DNNs), which are sophisticated algorithms that process unstructured data such as images, speech, and text. DNNs are also computationally expensive. For example, it takes almost 8 billion calculations to analyze one image using ResNet 50, a popular DNN.

There are many hardware options to run DNNs today, most commonly on CPUs and GPUs. Azure Machine Learning service brings customers the cutting-edge innovation that originated in Microsoft Research (featured in this recent Fast Company article), to run DNNs on reconfigurable hardware called FPGAs. By integrating this capability and the ONNX runtime in Azure Machine Learning service, we see vast improvements in the latencies of models.

Bringing it together

Azure Machine Learning service now brings the power of accelerated AI models directly to Data Box Edge. Let’s take the example of a manufacturing assembly line scenario, where cameras are photographing products at various stages of development.

The pictures are sent from the manufacturing line to Data Box Edge inside your factory, where AI models trained, containerized and deployed to FPGA using Azure Machine Learning service, are available. Data Box Edge is registered with Azure IoT Hub, so you can control which models you want deployed. Now you have everything you need to process incoming pictures in near real-time to detect manufacturing defects. This enables the machines and assembly line managers to make time-sensitive decisions about the products, improving product quality, and decreasing downstream production costs.

Join the preview

Azure Machine Learning service is already generally available today. To join the preview for containerization of hardware accelerated AI models, fill out the request form and get support on our forum.
Quelle: Azure

New updates to Azure AI expand AI capabilities for developers

As companies increasingly look to transform their businesses with AI, we continue to add improvements to Azure AI to make it easy for developers and data scientists to deploy, manage, and secure AI functions directly into their applications with a focus on the following solution areas:

Leveraging machine learning to build and train predictive models that improve business productivity with Azure Machine Learning.
Applying an AI-powered search experience and indexing technologies that quickly find information and glean insights with Azure Search.
Building applications that integrate pre-built and custom AI capabilities like vision, speech, language, search, and knowledge to deliver more engaging and personalized experiences with our Azure Cognitive Services and Azure Bot Service.

Today, we’re pleased to share several updates to Azure Cognitive Services that continue to make Azure the best place to build AI. We’re introducing a preview of the new Anomaly Detector Service which uses AI to identify problems so companies can minimize loss and customer impact. We are also announcing the general availability of Custom Vision to more accurately identify objects in images. 

From using speech recognition, translation, and text-to-speech to image and object detection, Azure Cognitive Services makes it easy for developers to add intelligent capabilities to their applications in any scenario. To this date more than a million developers have already discovered and tried Cognitive Services to accelerate breakthrough experiences in their application.

Anomaly detection as an AI service

Anomaly Detector is a new Cognitive Service that lets you detect unusual patterns or rare events in your data that could translate to identifying problems like credit card fraud.

Today, over 200 teams across Azure and other core Microsoft products rely on Anomaly Detector to boost the reliability of their systems by detecting irregularities in real-time and accelerating troubleshooting. Through a single API, developers can easily embed anomaly detection capabilities into their applications to ensure high data accuracy, and automatically surface incidents as soon as they happen.

Common use case scenarios include identifying business incidents and text errors, monitoring IoT device traffic, detecting fraud, responding to changing markets, and more. For instance, content providers can use Anomaly Detector to automatically scan video performance data specific to a customer’s KPIs, helping to identify problems in an instant. Alternatively, video streaming platforms can apply Anomaly Detector across millions of video data sets to track metrics. A missed second in video performance can translate to significant revenue loss for content providers that monetize on their platform.

Custom Vision: automated machine learning for images

With the general availability of Custom Vision, organizations can also transform their business operations quickly and accurately identifying objects in images.

Powered by machine learning, Custom Vision makes it easy and fast for developers to build, deploy, and improve custom image classifiers to quickly recognize content in imagery. Developers can train their own classifier to recognize what matters most in their scenarios, or export these custom classifiers to run them offline and in real time on iOS (in CoreML), Android (in TensorFlow), and many other devices on the edge. The exported models are optimized for the constraints of a mobile device providing incredible throughput while still maintaining high accuracy.

Today, Custom Vision can be used for a variety of business scenarios. Minsur, the largest tin mine in the western hemisphere, located in Peru, applies Custom Vision to create a sustainable mining practice by ensuring that water used in the mineral extraction process is properly treated for reuse on agriculture and livestock by detecting treatment foam levels. They used a combination of Cognitive Services Custom Vision and Azure video analytics to replace a highly manual process so that employees can focus on more strategic projects within the operation.

Screenshot of the Custom Vision platform, where you can train the model to detect unique objects in an image, such as your brand’s logo.

Starting today, Custom Vision delivers the following improvements:

High quality models – Custom Vision features advanced training with a new machine learning backend for improved performance, especially on challenging datasets and fine-grained classification. With advanced training, you can specify a compute time budget and Custom Vision will experimentally identify the best training and augmentation settings.
Iterate with ease – Custom Vision makes it simple for developers to integrate computer vision capabilities into applications with 3.0 REST APIs and SDKs. The end to end pipeline is designed to support the iterative improvement of models, so you can quickly train a model, prototype in real world conditions, and use the resulting data to improve the model which gets models to production quality faster.
Train in the cloud, run anywhere – The exported models are optimized for the constraints of a mobile device, providing incredible throughput while still maintaining high accuracy. Now, you can also export classifiers to support Azure Resource Manager (ARM) for Raspberry Pi 3 and the Vision AI Dev Kit.

For more information, visit the Custom Vision Service Release Notes.

Get started today

Today’s milestones illustrate our commitment to make the Azure AI platform suitable for every business scenario, with enterprise-grade tools that simplify application development, and industry leading security and compliance for protecting customers’ data.

To get started building vision and search intelligent apps, please visit the Cognitive Services site.
Quelle: Azure

Clean up files by built-in delete activity in Azure Data Factory

Azure Data Factory (ADF) is a fully-managed data integration service in Azure that allows you to iteratively build, orchestrate, and monitor your Extract Transform Load (ETL) workflows. In the journey of data integration process, you will need to periodically clean up files from the on-premises or the cloud storage server when the files become out of date. For example, you may have a staging area or landing zone, which is an intermediate storage area used for data processing during your ETL process. The data staging area sits between the data source stores and the data destination store. Given the data in staging areas are transient by nature, you need to periodically clean up the data in the staging area after the ETL process has being completed.

We are excited to share ADF built-in delete activity, which can be part of your ETL workflow to deletes undesired files without writing code. You can use ADF to delete folder or files from Azure Blob Storage, Azure Data Lake Storage Gen1, Azure Data Lake Storage Gen2, File System, FTP Server, sFTP Server, and Amazon S3.

You can find ADF delete activity under the “Move & Transform” section from the ADF UI to get started.

1. You can either choose to delete files or delete the entire folder. The deleted files and folder name can be logged in a csv file.

2. The file or folder name to be deleted can be parameterized, so that you have the flexibility to control the behavior of delete activity in your data integration flow.

3. You can delete expired files only rather than deleting all the files in one folder. For example, you may want to only delete the files which were last modified more than 30 days ago.

4. You can start from ADF template gallery to quickly deploy common use cases involving delete activity.

You are encouraged to give these additions a try and provide us with feedback. We hope you find them helpful in your scenarios. Please post your questions on Azure Data Factory forum or share your thoughts with us on Data Factory feedback site.
Quelle: Azure

Why IoT is not a technology solution—it's a business play

Enterprise leaders understand how important the Internet of Things (IoT) will be to their companies—in fact, according to a report by McKinsey & Company, 92 percent of them believe IoT will help them innovate products and improve operations by 2020. However, like many business-enabling systems, IoT is not without its growing pains. Even early adopters have concerns about the cost, complexity, and security implications of applying IoT to their businesses.

These growing pains can make it daunting for organizations to pick an entry point for applying IoT to their business.

Many companies start by shifting their thinking. It’s easy to get lost in technology, opting for platforms with the newest bells and whistles, and then leaning on those capabilities to drive projects. But sustainable change doesn’t happen that way—it happens when you consider business needs first, and then look at how the technology can fulfill those needs better than current processes can.

You might find it helpful to start by thinking about how IoT can transform your business. You know connected products will be an important part of your business model, but before you start building them, you need to make sure you understand where the market is headed so you can align innovation with emerging needs. After all, the biggest wins come when you can use emerging technology to create a “platform” undergirding products and services that can be extended into new opportunity areas.

To help you plan your IoT journey, we’re rolling out a four-part blog series. In the upcoming posts, we’ll cover how to create an IoT business case, overcome capability gaps, and simplify execution; all advice to help you maximize your gains with IoT.

Let’s get started by exploring the mindset it takes to build IoT into your business model.

Make sure business sponsors are invested

With any business-enabling system, organizations instinctively engage in focused exploration before they leap in. The business and IT can work together to develop a business case, identifying and prioritizing areas that can be optimized and provide real value. Involving the right business decision-makers will ensure you have sponsorship, budget, and commitment when it comes to applying IoT to new processes and systems and make the necessary course corrections as implementation grows and scales. Put your business leaders at the center of the discussion and keep them there.

Seize early-mover advantage

Organizations that are early in and commit to developing mastery in game-changing technologies may only see incremental gains in the beginning. But their leadership position often becomes propulsive, eventually creating platform business advantages that allow them to outdistance competitors for the long term. Don’t believe it? Just look at the history of business process automation, operational optimization, ecommerce, cloud services, digital business, and other tech-fueled trends to see how this has played out.

Consider manufacturing. The industry was a leader in operational optimization, using Six Sigma and other methodologies to strip cost and waste out of processes. After years of these efforts, process improvements became a game of inches with ever-smaller benefits.

Enter IoT. Companies have used IoT to achieve significant gains with improving production and streamlining operations in both discrete and process manufacturing. IoT can help companies predict changing market demand, aligning output to real needs. In addition, many manufacturing companies use IoT to help drive throughput of costly production equipment. Sensors and advanced analytics predict when equipment needs preventive maintenance, eliminating costly downtime.

How companies are using IoT today

Johnson Controls makes building-automation solutions, enabling customers to fine-tune energy use, lowering costs and achieving sustainability goals. The company built out its connected platform in the cloud with the Microsoft Azure IoT Suite to be able to aggregate, manage, and make sense of the torrent of facility data it receives. Through its Smart Connected Chillers initiative, Johnson Controls was able to identify a problem with a customer’s chiller plant, take corrective action, and prevent unplanned downtime that would have cost the customer $300,000 an hour.

IoT can also enable new business models. Adobe used to run its business with one-time software sales, but the company pivoted to online subscription sales as part of its drive to create a digital business, according to Forbes. While the move seemed risky at the time (and initially hurt revenue), Adobe’s prescient move has enabled it to dominate digital marketing, creative services, and document management. Now, of course, the software industry is predominantly Software as a Service (SaaS). Adobe is building on its success by pushing even deeper into analytics. The Adobe Experience Cloud uses Microsoft Azure and Microsoft Dynamics 365 to provide businesses with a 360-degree customer view and the tools to create, deliver, and manage digital experiences and a globally scalable cloud platform.

When connections with customers are constant and always adding value—everyone wins.

Think about IoT as a business enabler

It’s helpful to constantly stress to your team that IoT is a new way to enable a business strategically. IoT isn’t a bolt-on series of technology applications to incrementally optimize what you have. That may seem at odds with the prevailing wisdom to optimize current services. Yes, organizations should seek efficiencies, but only after they have considered where the business is headed, how things need to change to support targeted growth, and whether current processes can be improved or need to be totally transformed. In the case of Johnson Controls, service optimization is a core part of the company’s value proposition to its customers.

Yes, IoT can reinvent your business. And yes, constant, restless innovation can be expected until IoT is fully embedded in your business in a way that’s organic and self-sustaining. Adobe has used its digital platform to extend further and further into its customers’ businesses, providing value they can measure.

If you haven’t started with IoT, now is the right time, because your competitors are grappling with these very issues and creating smart strategies to play the IoT long game. Disruption is here and will only get more pronounced as platform leaders rocket ahead.

As you plan your journey with IoT, there’s help. In forthcoming blogs, we’ll be looking at:

Building a business case for IoT—Why this is the moment to be thinking about IoT and developing a solid business case to capture market opportunity and future-proof your organization.
Paving the way for IoT—Understanding and addressing what gaps need to be overcome to achieve IoT’s promise.
Becoming an IoT leader now—It’s simpler than you think to start with IoT. You can use what you have and SaaS-ify your approach with technology that makes it easy to connect, monitor, and manage your IoT assets at scale.

Need inspiration? Watch this short video to hear insights from IoT leaders Henrik Fløe of Grundfos, Doug Weber from Rockwell Automation, Michael MacKenzie from Schneider Electric, and Alasdair Monk of The Weir Group. 
Quelle: Azure

Azure Storage support for Azure Active Directory based access control generally available

We are pleased to share the general availability of Azure Active Directory (AD) based access control for Azure Storage Blobs and Queues. Enterprises can now grant specific data access permissions to users and service identities from their Azure AD tenant using Azure’s Role-based access control (RBAC).  Administrators can then track individual user and service access to data using Storage Analytics logs. Storage accounts can be configured to be more secure by removing the need for most users to have access to powerful storage account access keys.

By leveraging Azure AD to authenticate users and services, enterprises gain access to the full array of capabilities that Azure AD provides, including features like two-factor authentication, conditional access, identity protection, and more. Azure AD Privileged Identity Management (PIM) can also be used to assign roles “just-in-time” and reduce the security risk of standing administrative access.

In addition, developers can use Managed identities for Azure resources to deploy secure Azure Storage applications without having to manage application secrets.

When Azure AD authentication is combined with the new Azure Data Lake Storage Gen 2 capabilities, users can also take advantage of granular file and folder access control using POSIX-style access permissions and access control lists (ACL’s).

RBAC for Azure Resources can be used to grant access to broad sets of resources across a subscription, a resource group, or to individual resources like a storage account and blob container. Role assignments can be made through the Azure portal or through tools like Azure PowerShell, Azure CLI, or Azure Resource Manager templates.

Azure AD authentication is available from the standard Azure Storage tools including the Azure portal, Azure CLI, Azure PowerShell, Azure Storage Explorer, and AzCopy.

$ az login
Note, we have launched a browser for you to login. For old experience with device code, use "az login –use-device-code"
You have logged in. Now let us find all the subscriptions to which you have access…
[
{
"cloudName": "AzureCloud",
"id": "XXXXXXXX-YYYY-ZZZZ-AAAA-BBBBBBBBBBBB",
"isDefault": true,
"name": "My Subscription",
"state": "Enabled",
"tenantId": "00000000-0000-0000-0000-000000000000",
"user": {
"name": "cbrooks@microsoft.com",
"type": "user"
}
}
]
$ export AZURE_STORAGE_AUTH_MODE="login"
$ az storage blob list –account-name mysalesdata –container-name mycontainer –query [].name
[
"salesdata.csv"
]​

We encourage you to use Azure AD to grant users access to data, and to limit user access to the storage account access keys. A typical pattern for this would be to grant users the "Reader" role make the storage account visible to them in the portal along with the "Storage Blob Data Reader" role to grant read access to blob data. Users who need to create or modify blobs can be granted the "Storage Blob Data Contributor" role instead.

Developers are encouraged to evaluate Managed Identities for Azure resources to authenticate applications in Azure or Azure AD service principals for apps running outside Azure.

Azure AD access control for Azure Storage is available now for production use in all Azure cloud environments
Quelle: Azure

Azure Premium Block Blob Storage is now generally available

As enterprises accelerate cloud adoption and increasingly deploy performance sensitive cloud-native applications, we are excited to announce general availability of Azure Premium Blob Storage. Premium Blob Storage is a new performance tier in Azure Blob Storage for block blobs and append blobs, complimenting the existing Hot, Cool, and Archive access tiers. Premium Blob Storage is ideal for workloads that require very fast response times and/or high transactions rates, such as IoT, Telemetry, AI, and scenarios with humans in the loop such as interactive video editing, web content, online transactions, and more.

Premium Blob Storage provides lower and more consistent storage latency, providing low and consistent storage response times for both read and write operations across a range of object sizes, and is especially good at handling smaller blob sizes. Your application should be deployed to compute instances in the same Azure region as the storage account to realize low latency end-to-end. For more details on performance see, “Premium Block Blob Storage – a new level of performance.”

Figure 1 – Latency comparison of Premium and Standard Blob Storage

Premium Blob Storage is available with Locally-Redundant Storage (LRS) and comes with High-Throughput Block Blobs (HTBB), which provides very high and instantaneous write throughput when ingesting block blobs larger than 256KB.

Pricing and region availability

Premium Blob Storage has higher data storage cost, but lower transaction cost compared to data stored in the regular Hot tier. This makes it cost effective and can be less expensive for workloads with high transaction rates. Check out the pricing page for more details.

Premium Blob Storage is initially available in US East, US East 2, US Central, US West, US West 2, North Europe, West Europe, Japan East, Australia East, Korea Central, and Southeast Asia regions with more regions to come. Stay up to date on region availability through the Azure global infrastructure page.

Platform interoperability

At present, data stored in Premium cannot be tiered to Hot, Cool, or Archive access tiers. We are working on supporting object tiering in the future. To move data, you can synchronously copy blobs using the new PutBlockFromURL API (sample code) or AzCopy v10, which supports this API. PutBlockFromURL synchronously copies data server side, which means all data movement happens inside Azure Storage.

In addition, Storage Analytics Logging, Static website, and Lifecycle Management (preview) are not currently available with Premium Blob Storage.

Next steps

To get started with Premium Blob Storage you provision a Block Blob storage account in your subscription and start creating containers and blobs using the existing Blob Service REST API and/or any existing tools such as AzCopy or Azure Storage Explorer.

We are very excited about being able to deliver Azure Premium Blob Storage with low and consistent latency and look forward to hearing your feedback at premiumblobfeedback@microsoft.com. To learn more about Blob Storage please visit our product page.
Quelle: Azure