https://blogs.msdn.microsoft.com/azuredatalake/2018/04/23/keeping-data-lake-costs-under-control-creating-alerts-for-aus-usage-thresholds/Source: https://blogs.msdn.microsoft.com/azuredatalake/2018/04/23/keeping-data-lake-costs-under-control-creating-alerts-for-aus-usage-thresholds/ Have you ever been surprised by a larger-than-expected monthly Azure Data Lake Analytics bill? Creating alerts using Log Analytics will help you know when the bill is growing more than it should. In this post, I will READ MORE
Azure Security News at RSA Conference 2018
Last week, we made several Azure Security announcements in conjunction with RSA Conference 2018 in San Francisco:
Introducing Microsoft Azure Sphere: Secure and power the intelligent edge – Microsoft Azure Sphere is a new solution for creating highly-secured, Internet-connected microcontroller (MCU) devices. Azure Sphere includes three components that work together to protect and power devices at the intelligent edge: Azure Sphere certified microcontrollers (MCUs), Azure Sphere OS, and Azure Sphere Security Service.
Microsoft Azure Sphere Leadership Vision – Microsoft product and business leaders introduce Azure Sphere, the latest IoT offering from Microsoft that extends security and new consumer experiences to a whole new class of devices at the intelligent edge.
The 3 ways Azure improves your security – Learn how Azure provides value in three key areas – a secure foundation that is provided by Microsoft, built-in security controls to help you quickly configure security across the full-stack, and unique intelligence at cloud scale to help you safeguard data and respond to threats in real-time. Announcing new Azure Security Center capabilities at RSA 2018 – Azure Security Center provides centralized visibility of the
We recently released a port of our Azure IoT Hub C SDK for iOS platform. Whether your iOS project is written in Swift or Objective-C, you can leverage our device SDK and service SDK directly and begin turning your iOS device into an IoT device! Our libraries are available on CocoaPod, a popular package manager for iOS, and the source code is available on GitHub.
iOS devices are traditionally not viewed as IoT devices, but recently, they are getting traction in the IoT space. Here are some of the interesting scenarios we gathered from our industry customers during the preview phase:
iOS device as the gateway for leaf devices or sensors on the factory floor. iOS device in a meeting room, which acts as an end IoT device to send and receive messages from Azure IoT Hub. iOS device to view the visualization of IoT telemetry. iOS device to manage IoT Hub operations.
So, what is in the box? If you have interacted with our Azure IoT Hub C SDK before, this would be familiar to you! Our C SDK is written in C99 for maximum portability to various platforms. The porting process involves writing a thin adoption layer for
https://blogs.microsoft.com/iot/2018/04/23/hannover-messe-2018-manufacturers-put-their-trust-in-microsofts-industrial-iot-platform/Source: https://blogs.microsoft.com/iot/2018/04/23/hannover-messe-2018-manufacturers-put-their-trust-in-microsofts-industrial-iot-platform/ Microsoft once again will have a major presence at Digital Factory Hall at Hannover Messe 2018, the world’s largest annual manufacturing exhibition in Hannover, Germany. Our booth is filled with examples of the great work our READ MORE
Today we’re pleased to announce two key capabilities that Azure Time Series Insights will be delivering later this year:
A cost-effective long-term storage that enables a cloud-based solution to trend years’ worth of time series data pivoted on devices/tags. A device-based (also known industry-wide as “tag-based”) user experience backed by a time series model to contextualize raw time series data with device metadata and domain hierarchies.
Additionally, Time Series Insights will be integrating with advanced machine learning and analytics tools like Spark and Jupyter notebooks to help customers tackle time series data challenges in new ways. Data scientists and process engineers in industries like oil & gas, power & utility, manufacturing, and building management rely on time series data solutions for critical tasks like storage, data analysis, and KPI tracking and they’ll be able to do this using Time Series Insights .
Time series model and tag-centric experience
Time Series Insights’ current user interface is great for data scientists and analysts. However, process engineers and asset operators may not always find this experience natural to use. To address this, we are adding a device-based user experience to the Time Series Insights explorer. This new interface and the underlying time series
This blog post was authored by Rodrigo de Carvalho, Product Marketing Manager, Microsoft Azure.
Gartner’s Magic Quadrant for Enterprise Integration Platform as a Service (eiPaaS), 2018 positions Microsoft as a leader and it reflects Microsoft’s ability to execute and completeness of vision.
Microsoft’s global presence, strong growth, and platform versatility provides customers the confidence to choose Azure as the cloud platform to automate, integrate, and optimize their business processes by connecting on-premises applications, SaaS, data, and to API-enable applications with managed integration services.
Integration Platform-as-a-Service at enterprise-scale
By integrating applications and data with partners, suppliers or customers, organizations optimize trade and information exchange driving business agility. Process automation, Enterprise Application Integration (EAI), Business-to-Business (B2B) transactions, Electronic Data Interchange (EDI), and Application Programing Interface (API) management are all areas in which organizations leverage Azure’s integration capabilities.
With Azure, organizations enhance productivity with business processes automation, SaaS, and on-premises application integration leveraging the most common out-of-the-box connectors for Azure services, Office 365, Dynamics CRM, among others.
“To build the highest quality product causing the least amount of harm.”
– Drew Story, Solution Architect, Patagonia
Also, organizations use Azure to optimize the exchange of electronic messages in business-to-business transactions, even
Industrial IoT is the largest IoT opportunity. At Microsoft, we serve this vertical by offering an Industrial IoT Cloud Platform Reference Architecture, which we have conveniently bundled into an open-source Azure IoT Suite solution called Connected Factory and launched it at HMI 2017 a year ago.
Since then, we continued our collaboration with the OPC Foundation, the non-profit organization developing the OPC UA Industrial Interoperability Standard, and added many new open-source contributions to their Github page, further extending our lead as the largest contributor of open-source software to the OPC Foundation by a factor of 10. We have also successfully certified the open-source, cross-platform .Net Standard OPC UA reference stack for compliance. This was a crucial step in our open-source OPC UA journey as Connected Factory uses this stack internally. We also managed to reduce the monthly Azure consumption cost of Connected Factory due to the new pricing structure of Azure IoT Hub recently announced.
Although Connected Factory is extremely popular with both machine builders and manufacturers, we hear from time to time that it is still difficult to connect real machines to it and at the same time make these machines secure for IoT applications. Therefore, we have added
https://blogs.msdn.microsoft.com/sql_server_team/replication-enhancement-distribution-database-in-availability-group/Source: https://blogs.msdn.microsoft.com/sql_server_team/replication-enhancement-distribution-database-in-availability-group/ SQL Server replication uses the publisher, distributor, and subscriber paradigm to provide logical data replication capability between different SQL Server instances, and sometime with heterogeneous data source or data destination. Replication uses the distribution databases hosted on READ MORE
This post is authored by John ‘JG’ Chirapurath, General Manager, Azure Data.
Since SQL Server 2017 on Linux was made generally available on October 2, 2017, we have seen tremendous growth in adoption. We’ve had ~ 5 million Docker pulls for SQL Server 2017 on Linux and are seeing strong affinity with our customers who are on other database systems running on Linux as well as customers consolidating on Linux as their operating system. Additionally, we’ve had a great response from the open source developer community.
SQL Server 2017 is supported on Red Hat Enterprise Linux (RHEL), SUSE Linux Enterprise Server (SLES), and Ubuntu. It is also supported as a Docker image, which can run on Docker Engine on Linux or Docker for Windows/Mac.
In the path forward to deliver choice, today Microsoft and SUSE are announcing some great offers for our mutual customers both on-premises and on Azure to adopt SQL Server 2017 on Linux as their database of choice.
Microsoft and SUSE have a decade long relationship, powering enterprises focused on interoperability and high-performance solutions. With SQL Server 2017 running on SUSE Enterprise Linux Server, we are bringing the most cost-effective path for our customers. If you combine
This post is authored by Mathew Salvaris and Fidan Boylu Uz, Senior Data Scientists at Microsoft.
One of the major challenges that data scientists often face is closing the gap between training a deep learning model and deploying it at production scale. Training of these models is a resource intensive task that requires a lot of computational power and is typically done using GPUs. The resource requirement is less of a problem for deployment since inference tends not to pose as heavy a computational burden as training. However, for inference, other goals also become pertinent such as maximizing throughput and minimizing latency. When inference speed is a bottleneck, GPUs show considerable performance gains over CPUs. Coupled with containerized applications and container orchestrators like Kubernetes, it is now possible to go from training to deployment with GPUs faster and more easily while satisfying latency and throughput goals for production grade deployments.
In this tutorial, we provide step-by-step instructions to go from loading a pre-trained Convolutional Neural Network model to creating a containerized web application that is hosted on Kubernetes cluster with GPUs on Azure Container Service (AKS). AKS makes it quick and easy to deploy and manage containerized applications without much