Category Archives : DevOps



Building cloud-native applications with Azure and HashiCorp

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




Preview of custom content in Azure Policy guest configuration

Today we are announcing a preview of a new feature of Azure Policy. The guest configuration capability, which audits settings inside Linux and Windows virtual machines (VMs), is now ready for customers to author and publish custom content.

The guest configuration platform has been generally available for built-in content provided by Microsoft. Customers are using this platform to audit common scenarios such as who has access to their servers, what applications are installed, if certificates are up to date, and whether servers can connect to network locations.

Starting today, customers can use new tooling published to the PowerShell Gallery to author, test, and publish their own content packages both from their developer workstation and from CI/CD platforms such as Azure DevOps.

For example, if you are running an application on an Azure virtual machine that was developed by your organization, you can audit the configuration of that application in Azure and be notified when one of the VMs in your fleet is not compliant.

This is also an important milestone for compliance teams who need to audit configuration baselines. There is already a built-in policy to audit Windows machines using Microsoft’s recommended security configuration baseline.  Custom content expands the



Aug 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




Announcing the preview of Azure Actions for GitHub

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.

The process requires using a service principal, which can be generated using the Azure CLI, as per instructions. Use the GitHub Actions’ built-in secret store for




Azure Stream Analytics now supports MATCH_RECOGNIZE

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




When to use Azure Service Health versus the status page
When to use Azure Service Health versus the status page

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




Automate MLOps workflows with Azure Machine Learning service CLI

This blog was co-authored by Jordan Edwards, Senior Program Manager, Azure Machine Learning

This year at Microsoft Build 2019, we announced a slew of new releases as part of Azure Machine Learning service which focused on MLOps. These capabilities help you automate and manage the end-to-end machine learning lifecycle.

Historically, Azure Machine Learning service’s management plane has been via its Python SDK. To make our service more accessible to IT and app development customers unfamiliar with Python, we have delivered an extension to the Azure CLI focused on interacting with Azure Machine Learning.

While it’s not a replacement for the Azure Machine Learning service Python SDK, it is a complimentary tool that is optimized to handle highly parameterized tasks which suit themselves well to automation. With this new CLI, you can easily perform a variety of automated tasks against the machine learning workspace, including:

Datastore management Compute target management Experiment submission and job management Model registration and deployment

Combining these commands enables you to train, register their model, package it, and deploy your model as an API. To help you quickly get started with MLOps, we have also released a predefined template in Azure Pipelines. This template allows you




Virtual machine scale set insights from Azure Monitor

In October 2018 we announced the public preview of Azure Monitor for Virtual Machines (VMs). At that time, we included support for monitoring your virtual machine scale sets from the at scale view under Azure Monitor.

Today we are announcing the public preview of monitoring your Windows and Linux VM scale sets from within the scale set resource blade. This update includes several enhancements:

In-blade monitoring for your scale set with “Top N”, aggregate, and list views across the entire scale set. Drill down experience to identify issues on a particular scale set instance. Updated mapping UI to display the entire dependency diagram across your scale set while supporting drill down maps for a single instance. UI based enablement of monitoring from the scale set resource blade. Updated examples for enabling monitoring using Azure Resource Manager templates. Use of policy to enable monitoring for your scale set. Performance

The performance views are powered using log analytics queries, offering “Top N”, aggregate, and list views to quickly find outliers or issues in your scale set based on guest level metrics for CPU, available memory, bytes sent and received, and logical disk space used. 

These views will help you quickly determine if a




Microsoft and Truffle partner to bring a world-class experience to blockchain developers

Last month, Microsoft released Azure Blockchain Service making it easy for anyone to quickly setup and manage a blockchain network and providing a foundation for developers to build a new class of multi-party blockchain applications in the cloud.

To enable end-to-end development of these new apps, we’ve collaborated with teams from Visual Studio Code to Azure Logic Apps and Microsoft Flow to Azure DevOps, to deliver a high-quality experience that integrates Microsoft tools developers trust and open-source tools they love.

As we looked at the open source projects for Ethereum-based blockchains, we saw Truffle addressing core needs of developers looking to create, compile, test, and manage smart contract code. We kicked off our relationship in 2018 by co-authoring guidance for using Truffle for consortium DevOps and incorporating Truffle-based tooling in our Azure Blockchain Development Kit for Ethereum.

This week, we doubled down on our relationship by announcing an official partnership between our organizations to bring Truffle blockchain tools for developer experience and DevOps to Microsoft Azure. This will manifest not just in Visual Studio and Azure DevOps, but also upcoming tools from Truffle such as Truffle Teams. Through this partnership, developers working in Truffle environments will have access to Azure




Announcing service monitor alliances for Azure Deployment Manager

Azure Deployment Manager is a new set of features for Azure Resource Manager that greatly expands your deployment capabilities. If you have a complex service that needs to be deployed to several regions, if you’d like greater control over when your resources are deployed in relation to one another, or if you’d like to limit your customer’s exposure to bad updates by catching them while in progress, then Deployment Manager is for you. Deployment Manager allows you to perform staged rollouts of resources, meaning they are deployed region by region in an ordered fashion.

During Microsoft Build 2019, we announced that Deployment Manager now supports integrated health checks. This means that as your rollout proceeds, Deployment Manager will integrate with your existing service health monitor, and if during deployment unacceptable health signals are reported from your service, the deployment will automatically stop and allow you to troubleshoot.

In order to make health integration as easy as possible, we’ve been working with some of the top service health monitoring companies to provide you with a simple copy/paste solution to integrate health checks with your deployments. If you’re not already using a health monitor, these are great solutions to start with: