https://azure.microsoft.com/blog/hdinsight-support-in-azure-cli-now-out-of-preview/We are pleased to share that support for HDInsight in Azure CLI is now generally available. The addition of the az hdinsight command group allows you to easily manage your HDInsight clusters using simple commands while taking advantage of all READ MORE
With each passing year, more and more developers are building cloud-native applications. As developers build more complex applications they are looking to innovators like Microsoft Azure and HashiCorp to reduce the complexity of building and operating these applications. HashiCorp and Azure have worked together on a myriad of innovations. Examples of this innovation include tools that connect cloud-native applications to legacy infrastructure and tools that secure and automate the continuous deployment of customer applications and infrastructure. Azure is deeply committed to being the best platform for open source software developers like HashiCorp to deliver their tools to their customers in an easy-to use, integrated way. Azure innovation like the managed applications platform that power HashiCorp’s Consul Service on Azure are great examples of this commitment to collaboration and a vibrant open source startup ecosystem. We’re also committed to the development of open standards that help these ecosystems move forward and we’re thrilled to have been able to collaborate with HashiCorp on both the CNAB (Cloud Native Application Bundle) and SMI (Service Mesh Interface) specifications.
Last year at HashiConf 2018, I had the opportunity to share how we had started to integrate Terraform and Packer into the Azure platform. I’m incredibly
Congratulations to the PyTorch community on the release of PyTorch 1.2! Last fall, as part of our dedication to open source AI, we made PyTorch one of the primary, fully supported training frameworks on Azure. PyTorch is supported across many of our AI platform services and our developers participate in the PyTorch community, contributing key improvements to the code base. Today we would like to share the many ways you can use PyTorch 1.2 on Azure and highlight some of the contributions we’ve made to help customers take their PyTorch models from training to production.
PyTorch 1.2 on Azure
Getting started with PyTorch on Azure is easy and a great way to train and deploy your PyTorch models. We’ve integrated PyTorch 1.2 in the following Azure services so you can utilize the latest features:
Azure Machine Learning service – Azure Machine Learning streamlines the building, training, and deployment of machine learning models. Azure Machine Learning’s Python SDK has a dedicated PyTorch estimator that makes it easy to run PyTorch training scripts on any compute target you choose, whether it’s your local machine, a single virtual machine (VM) in Azure, or a GPU cluster in Azure. Learn how to train Pytorch
It’s always been a tricky business to handle mission-critical processes. Much of the technical debt that companies assume comes from having to architect systems that have multiple layers of redundancy, to mitigate the chance of outages that may severely impact customers. The process of both architecting and subsequently maintaining these systems has resulted in huge losses in productivity and agility throughout many enterprises across all industries.
The solutions that cloud computing provides help enterprises shift away from this cumbersome work. Instead of spending countless weeks or even months trying to craft an effective solution to the problem of handling critical workloads, cloud providers such as Azure now provide an out-of-the-box way to run your critical processes, without fear of outages, and without incurring costs associated with managing your own infrastructure.
One of the latest innovations in this category, developed by the Azure Logic Apps team, is a new SAP connector that helps companies easily integrate with the ERP systems that are critical to the day-to-day success of a business. Often, implementing these solutions can take teams of people months to get right. However, with the SAP connector from Logic Apps, this process often only takes days, or even hours!
https://azure.microsoft.com/blog/announcing-the-general-availability-of-python-support-in-azure-functions/Python support for Azure Functions is now generally available and ready to host your production workloads across data science and machine learning, automated resource management, and more. You can now develop Python 3.6 apps to run on the cross-platform, open-source READ MORE
The Azure Cosmos DB team is announcing the general availability of version 3 of the Azure Cosmos DB .NET SDK, released in July. Thank you to all who gave feedback during our preview.
In this post, we’ll walk through the latest improvements that we’ve made to enhance the developer experience in .NET SDK v3.
//Using .NET CLI dotnet add package Microsoft.Azure.Cosmos //Using NuGet Install-Package Microsoft.Azure.Cosmos What is Azure Cosmos DB?
Azure Cosmos DB is a globally distributed, multi-model database service that enables you to read and write data from any Azure region. It offers turnkey global distribution, guarantees single-digit millisecond latencies at the 99th percentile, 99.999 percent high availability, and elastic scaling of throughput and storage.
What is new in Azure Cosmos DB .NET SDK version 3?
Version 3 of the SDK contains numerous usability and performance improvements, including a new intuitive programming model, support for stream APIs, built-in support for change feed processor APIs, the ability to scale non-partitioned containers, and more. The SDK targets .NET Standard 2.0 and is open sourced on GitHub.
For new workloads, we recommend starting with the latest version
On Thursday, August 8, 2019, GitHub announced the preview of GitHub Actions with support for Continuous Integration and Continuous Delivery (CI/CD). Actions makes it possible to create simple, yet powerful pipelines and automate software compilation and delivery. Today, we are announcing the preview of Azure Actions for GitHub.
With these new Actions, developers can quickly build, test, and deploy code from GitHub repositories to the cloud with Azure.
You can find our first set of Actions grouped into four repositories on GitHub, each one containing documentation and examples to help you use GitHub for CI/CD and deploy your apps to Azure.
azure/actions (login): Authenticate with an Azure subscription. azure/appservice-actions: Deploy apps to Azure App Services using the features Web Apps and Web Apps for Containers. azure/container-actions: Connect to container registries, including Docker Hub and Azure Container Registry, as well as build and push container images. azure/k8s-actions: Connect and deploy to a Kubernetes cluster, including Azure Kubernetes Service (AKS). Connect to Azure
The login action (azure/actions) allows you to securely connect to an Azure subscription.
MATCH_RECOGNIZE in Azure Stream Analytics significantly reduces the complexity and cost associated with building, modifying, and maintaining queries that match sequence of events for alerts or further data computation.
What is Azure Stream Analytics?
Azure Stream Analytics is a fully managed serverless PaaS offering on Azure that enables customers to analyze and process fast moving streams of data and deliver real-time insights for mission critical scenarios. Developers can use a simple SQL language, extensible to include custom code, in order to author and deploy powerful analytics processing logic that can scale-up and scale-out to deliver insights with milli-second latencies.
Traditional way to incorporate pattern matching in stream processing
Many customers use Azure Stream Analytics to continuously monitor massive amounts of data, detecting sequence of events and deriving alerts or aggregating data from those events. This in essence is pattern matching.
For pattern matching, customers traditionally relied on multiple joins, each one detecting a single event in particular. These joins are combined to find a sequence of events, compute results or create alerts. Developing queries for pattern matching is a complex process and very error prone, difficult to maintain and debug. Also, there are limitations when trying to express more complex
If you’re experiencing problems with your applications, a great place to start investigating solutions is through your Azure Service Health dashboard. In this blog post, we’ll explore the differences between the Azure status page and Azure Service Health. We’ll also show you how to get started with Service Health alerts so you can stay better informed about service issues and take action to improve your workloads’ availability.
How and when to use the Azure status page
The Azure status page works best for tracking major outages, especially if you’re unable to log into the Azure portal or access Azure Service Health. Many Azure users visit the status page regularly. It predates Azure Service Health and has a friendly format that shows the status of all Azure services and regions at a glance.
The Azure status page, however, doesn’t show all information about the health of your Azure services and regions. The status page isn’t personalized, so you need to know exactly which services and regions you’re using and locate them in the grid. The status page also doesn’t include information about non-outage events that could affect your availability. For example, planned maintenance events and health advisories (think service retirements
As digital transformation expands beyond the walls of one company and into processes shared across organizations, businesses are looking to blockchain as a way to share workflow data and logic.
This spring we introduced Azure Blockchain Service, a fully-managed blockchain service that simplifies the formation, management, and governance of consortium blockchain networks. With a few simple clicks, users can create and deploy a permissioned blockchain network and manage consortium membership using an intuitive interface in the Azure portal.
To help developers building applications on the service, we also introduced our Azure Blockchain development kit for Ethereum. Delivered via Visual Studio Code, the dev kit runs on all major operating systems, and brings together the best of Microsoft and open source blockchain tooling, including deep integration with leading OSS tools from Truffle. These integrations enable developers to create, compile, test, and manage smart contract code before deploying it to a managed network in Azure.
We’re constantly looking and listening to feedback for areas where we can lean in and help developers go further, faster. This week for TruffleCon, we’re releasing some exciting new features that make it easier than ever to build blockchain applications:
Interactive debugger: Debugging of Ethereum smart contracts,