Microsoft Azure Monitor Insights
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First of all Thank you for following me and Sharing Microsoft Cloud and Datacenter Management content on Social Media 🙂 Sharing & Learning Together is Better.
Here some work I did for the Community in 2018 :
I will continue every day sharing knowledge with the Community and continue my Free work on MVPbuzz Friday for Education to get Azure Cloud Technology in the Classroom for Teachers and Students.
The trend I see for 2019 is more Infrastructure and Security by Code with Microsoft Azure DevOps
and of course you have to be in Control with Microsoft Azure Monitor
I will write a blogpost in January 2019 about Microsoft Azure Hub-Spoke model by Enterprise Design 4 of 4 : Optimize your Azure Workload.
More Items in 2019 to come :
2019 will be a Great year again with New Microsoft Technologies and Features for your business.
Azure DevTest Labs can be used to implement many key scenarios in addition to dev/test. One of those scenarios is to set up a lab for training. Azure DevTest Labs allows you to create a lab where you can provide custom templates that each trainee can use to create identical and isolated environments for training. You can apply policies to ensure that training environments are available to each trainee only when they need them and contain enough resources – such as virtual machines – required for the training. Finally, you can easily share the lab with trainees, which they can access in one click.
To Create your own DevTest Lab is easy in Microsoft Azure subscription :
Select Developer tools and then DevTest Labs
Give you DevTest Lab a Name and Resource
I already got it installed with some Virtual Machines.
When you go to Configuration and Policies you can configure your DevTest LAB for your Users.
From here you can manage and Configure your DevTest LAB.
Costs per Resource and who is the Owner
You can give your DevTest LAB Users full Control on Virtual Machine Sizes, but then you have to watch your Costs.
To keep you in Control you can decide which VM sizes can be selected by the DevTest LAB Users. From small standard A2 VM
or Powerful GS5 Virtual Machine.
Then you can select how much Virtual Machines can be selected by the DevTest LAB User or How Much Virtual Machines can be added to a Complete DevTest LAB :
Virtual Machines Per User
Virtual Machines per LAB
Here you can make a DevTest LAB Announcement for the Users.
To keep you Costs in a efficient way in Control, you can Auto Shutdown the Complete LAB and Start it with a Scheduler again.
Then you don’t pay for Compute when It’s not in use, this keeps your total costs low.
Important for your Azure DevTest LAB are the Images form the Market Place but you can also upload your own custom images :
Azure Market Place
Of course you can add also Repositories to your Azure DevTest LAB :
When you installed this all, you can configure your Identity and Access Management for your Azure DevTest LAB Users.
I Gave Student01 the Role DevTest Lab User.
When you Login with the Azure DevTest LAB User you see your Resources and the LAB.
In the Activity Log you can see what is happening in your LAB.
For Teachers in Education is Microsoft Azure DevTest LAB a Great solution to work with IT Students and Develop or making there own Projects for School.
Here you see how easy you can role out a Kali-Linux Virtual Machine in your Azure DevTest LAB
Select the Kali-Linux Image
Select your Virtual Machine Settings
Here you can select Artifacts for in your VM
You can download your JSON ARM Template here
Your Kali-Linux VM is Creating in your Azure DevTest LAB.
I like Microsoft Azure DevTest LAB a lot and I hope you too 🙂
Overview Azure Virtual Datacenter is an approach to making the most of the Azure cloud platform’s capabilities while respecting your existing security and networking policies. When deploying enterprise workloads to the cloud, IT organizations and business units must balance governance with developer agility. Azure Virtual Datacenter provides models to achieve this balance with an emphasis on governance. Deploying workloads to the cloud introduces the need to develop and maintain trust in the cloud to the same degree you trust your existing datacenters. The first model of Azure Virtual Datacenter guidance is designed to bridge that need through a locked-down approach to virtual infrastructures. This approach isn’t for everyone. It’s specifically designed to guide enterprise IT groups in extending their on-premises infrastructure to the Azure public cloud. We call this approach the trusted datacenter extension model. Over time, several other models will be offered, including those that allow secure Internet access directly from a virtual datacenter.
In the Azure Virtual Datacenter model, you can apply isolation policies, make the cloud more like the physical datacenters you know, and achieve the levels of security and trust you need. Four components any enterprise IT team would recognize make it possible: software-defined networking, encryption, identity management, and the Azure platform’s underlying compliance standards and certifications. These four are key to making a virtual datacenter a trusted extension of your existing infrastructure investment. Central to this model is the idea that your cloud infrastructure has isolation boundaries that can be thought of as your corporate namespace. Think of it as your isolated cloud within Azure. Within this virtual boundary, security controls, network policies, and compliance come together, providing you with an IT infrastructure on Azure capable of securely integrating cloud resources with your existing on-premises datacenter. You can deploy new virtual workspaces in the virtual datacenter much as you would deploy additional capacity to your physical datacenter. These virtual workspaces are self-contained
Environments where workloads can run independently, and workload teams can get workspace specific access. Workspaces enable teams to build solutions and manage workloads with great freedom while adhering to the overall access and security policies defined in the central IT infrastructure. This guide is intended for enterprise IT architects and executives. Using the lens of the physical datacenter, the guide discusses an approach to designing secure, trusted virtual datacenters on the Azure platform. Azure Virtual Datacenter is not a specific product or service but rather a way to think about cloud infrastructures. It offers proven practices and guidance to help smooth your migration to the cloud. At the end of this guide, you can learn about the upcoming Virtual Datacenter Automation guidance. This guidance includes a collection of scripts and Azure Resource Manager templates that will help you build an Azure Virtual Datacenter using the trusted extension model.
You can download this Awesome Microsoft whitepaper Azure Virtual Datacenter here
The Microsoft Ignite 2017 App s available
Don’t miss this Great Microsoft Ignite 2017 Event in Orlando Florida September 25-29, 2017 and Register for the last passes here
Have a look at the session Catalog and Choose your favorite topics at Microsoft Ignite 2017 here
Have lot’s of Fun and Great sessions to LEARN from with Awesome new Microsoft Technology !
Follow Microsoft Ignite on Twitter => @MS_Ignite
The Microsoft Data Science Virtual Machine (DSVM) is a powerful data science development environment that enables you to perform various data exploration and modeling tasks. The environment comes already built and bundled with several popular data analytics tools that make it easy to get started quickly with your analysis for On-premises, Cloud or hybrid deployments. The DSVM works closely with many Azure services and is able to read and process data that is already stored on Azure, in Azure SQL Data Warehouse, Azure Data Lake, Azure Storage, or in Azure Cosmos DB. It can also leverage other analytics tools such as Azure Machine Learning and Azure Data Factory.
The ‘Data Science Virtual Machine (DSVM)’ is a ‘Windows Server 2016 with Containers’ VM & includes popular tools for data exploration, analysis, modeling & development.
Tools for ML model operationalization as web services in the cloud, using Azure ML or Microsoft R Server.
Creating the Azure DSVM with Windows Server 2016
I use this Azure DSVM for testing and I will enable Auto-Shutdown Scheduler
(to save money)
The following software is by default installed in the Azure DSVM :
My Azure Data Science Virtual Machine in the Cloud 😉
From here you can configure your Data Management Gateway.
Microsoft Data Management Gateway connects on-premises data sources to cloud services for consumption. With Microsoft cloud services, such as Power BI for Office 365 and Azure Data Factory you get benefits including fast deployment, low maintenance cost, and flexible billing model while keeping your enterprise data on-premises. With Data Management Gateway, you can connect on-premises data to cloud services in a secure and managed way, to respond more quickly to changing business needs with a flexible, hybrid cloud platform. You can benefit from Microsoft cloud services while you keep your business running with the on-premises data.
More information about the Data Management Gateway :
Running Azure Data Science VM
Portal Overview of the Azure DSVM
From the Microsoft Azure Portal you can add Resource Extensions to your Azure Data Science VM.
From here you can add the following Extensions :
Here you can find more Technical information about Microsoft Azure Data Science Virtual Machine :