We’re happy to introduce the new Grafana integration with Microsoft Azure Monitor logs. This integration is achieved through the new Log Analytics plugin, now available as part of the Azure Monitor data source.
The new plugin continues our promise to make Azure’s monitoring data available and easy to consume. Last year, in the v1 of this data source we exposed Azure Monitor metric data in Grafana. While you can natively consume all logs in Azure Monitor Log Analytics, our customers also requested to make logs available in Grafana. We have heard this request and partnered with Grafana to enable you to use OSS tools more on Azure.
The new plugin allows you to display any data available in Log Analytics, such as logs related to virtual machine performance, security, Azure Active Directory which has recently integrated with Log Analytics, and many other log types including custom logs.
How can I use it?
The new plugin requires Grafana version 5.3 or newer. After the initial data source configuration, you can start embedding Azure Monitor logs in your dashboards and panels easily, simply select the service Azure Log Analytics and your workspace, then provide a query. You can reuse any existing queries
Every platform has limits, workstations and physical servers have resource boundaries, APIs may be rate-limited, and even the perceived endlessness of the virtual public cloud enforces limitations that protect the platform from overuse or misuse. You can learn more about these limitations by visiting our documentation, “Azure subscription and service limits, quotas, and constraints.” When working on scenarios that take platforms to their extreme, those limits become real and therefore thought should be put into overcoming them.
The following post includes essential notes taken from my work with Mike Kiernan, Mayur Dhondekar, and Idan Shahar. It also covers some iterations where we try to reach a limit of 10K virtual machines running on Microsoft Azure and explores the pros/cons of the different implementations.
Load tests at cloud scale
Load and stress tests before moving a new version to production are critical on the one hand, but pose a real challenge for IT on the other. This is because they require a considerable amount of resources to be available for only a short amount of time, every release-cycle. When purchased the infrastructure doesn’t justify its cost over extended periods, making this a perfect use-case for a public cloud platform where payment
As 2018 comes to an end, I look at the technology landscape. I look at the kinds of hybrid scenarios our customers are developing. for example, we see Airbus transforming aerospace with Microsoft Azure Stack and I realize that this year has been amazing for developers that design, develop, and maintain cloud-based apps. Azure Stack has improved support for DevOps practices. You can use Kubernetes containers. You can use API Profiles with Azure Resource Manager and the code of your choice. You can review walkthroughs and tutorials on getting up and running with a development practice using a continuous integration pipeline. With Azure Stack, your apps can be developed in the cloud. You can code once and deploy to environments in Azure or in your local data center.
We are now seeing some of your favorite services from Azure arrive on Azure Stack. The Azure Stack team is also excited to come together with other members of the Azure Edge family, which include Data Box Edge, IoT Edge, and Azure Sphere. If you didn’t get a chance to attend Ignite 2018’s session on the Intellgent Edge check out the “Delivering Intelligent Edge and Microsoft Azure Stack and Data Box” session.
https://azure.microsoft.com/blog/new-azure-pipelines-announcements-vs-code-extension-github-releases-and-more/Since we launched Azure Pipelines in September, we’ve seen strong growth in adoption of our cloud hosted build and deployment service. We’re also learning from many of the open source projects on GitHub starting to take advantage of unlimited build READ MORE
Today, we’re announcing a new integration between Azure Boards and GitHub. Development teams using GitHub can now take advantage of the rich project management capabilities offered by Azure Boards, including Kanban boards, backlogs, sprint planning tools, queries, and multiple work item types.
Code links when and where you need them
By linking GitHub commits and pull requests to your work items in Azure Boards, your code changes become just a click away from your planning artifacts. The development section of each work item (bug, story, task, etc.) shows the latest status of changes, and when more information is needed you can easily drill through to see more detail on GitHub.
Automatically link Azure Boards items from GitHub pull requests and commits
Keeping code and work items connected should be easy, which is why Azure Boards uses workflows known and loved by GitHub users. When you’re working on a code change and you want to relate it to the task you’re working on, simply mention the ID of the Azure Boards work item in the commit message, PR title, or PR description. The syntax used is “AB#[Work Item ID]” as shown below.
When the commit is pushed to
This blog post is co-authored by Parikshit Savjani, Senior Program Manager, Azure OSS Database service.
Spring is a well-known Java-based framework for building web and enterprise applications addressing the modern business needs. One of the advantages of using the Spring Boot framework is that it simplifies the data access from relational and NoSQL data stores. Spring Boot framework with MySQL Database backend is one of the established patterns to meet the online transactional processing needs of business applications. The modern business applications are built and deployed on cloud native microservice platforms like Azure Kubernetes service (AKS) moving away from traditional monolithic design to meet the elastic scale and portability needs. The databases on the other hand have more stateful requirements with atomicity, consistency, durability, resiliency, and zero data loss across failures. It is therefore more suited to run databases outside of Kubernetes environment on managed database services like Azure Database for MySQL service which meets these requirements.
Developers and customers can easily build and deploy their Java Spring Boot microservices application in Azure platform thereby improving developer productivity and enabling businesses to achieve more with the following solutions.
We are excited to share the preview availability of our Python packaging (PyPI) capabilities for Azure Artifacts and would love for you to give it a try.
Our team spends a lot of time surveying and listening to our customers to learn about their needs with regards to Azure Artifacts (packaging). In our research, it became evident that PyPI support was our most requested packaging type. This finding has motivated the hard work behind getting the PyPI feature ready for release.
If you work with Python packages in the scope of Azure DevOps, or more specifically with our Azure Pipelines CI/CD services, these new capabilities will allow you to accomplish the following:
Create a feed(s) associated with your project to store your packages. Upload Python packages to your feed using twine, flit support is being tested. Pull packages from your feed using pip. Integrate Python packages into your Azure Pipelines CI/CD using a task that simplifies the authentication for you. Include packages from the public index into your feed (Upstreams).
Please note that this feature is in preview, which means we’re currently still fine tuning it. We would love to hear feedback on your experience and how this functions
Last week we announced a preview of Docker support for Microsoft Azure Cognitive Services with an initial set of containers ranging from Computer Vision and Face, to Text Analytics. Here we will focus on trying things out, firing up a cognitive service container, and seeing what it can do. For more details on which containers are available and what they offer, read the blog post “Getting started with these Azure Cognitive Service Containers.”
You can run docker in many contexts, and for production environments you will definitely want to look at Azure Kubernetes Service (AKS) or Azure Service Fabric. In subsequent blogs we will dive into doing this in detail, but for now all we want to do is fire up a container on a local dev-box which works great for dev/test scenarios.
You can run Docker desktop on most dev-boxes, just download and follow the instructions. Once installed, make sure that Docker is configured to have at least 4G of RAM (one CPU is sufficient). In Docker for Windows it should look something like this:
Getting the images
The Text Analytics images are available directly from Docker Hub as follows:
Key phrase extraction extracts key talking
Today we’re excited to share the first release candidate (RC) of Azure DevOps Server 2019. Azure DevOps Server 2019 delivers the codebase of Microsoft Azure DevOps while being optimized for customers who prefer to self-host. This may be the case for some customers because they require Azure DevOps run on-premises, they require a guaranteed isolated instance of Azure DevOps, or because they want to run in regions where a hosted version of Azure DevOps is not available.
You can download Azure DevOps Server 2019 RC1 today.
Like the evolution of Team Foundation Server (TFS), Azure DevOps Server includes the new, fast, and clean Azure DevOps user interface with a multitude of new features. We’ll discuss some of the most beneficial features for our customers below, but you can check out our extensive release notes for all the features and information included in this initial release.
Added support for Azure SQL
Azure DevOps Server includes support for Azure SQL in addition to existing SQL Server support. This enables enterprises to self-host Azure DevOps in their own datacenter using an on-premises SQL Server. Customers now also have the option to self-host Azure DevOps in the cloud and take advantage of all the
https://azure.microsoft.com/blog/getting-started-with-azure-cognitive-services-in-containers/Building solutions with machine learning often requires a data scientist. Azure Cognitive Services enable organizations to take advantage of AI with developers, without requiring a data scientist. We do this by taking the machine learning models and the pipelines and READ MORE