The course is part of this learning path
Develop your skills for autoscaling on Azure with this course from Cloud Academy. Learn how to improve your teams and development skills and understand how they relate to scalable solutions. What's more, in this course you can analyze and execute how to deal with transient faults.
This Course is made up of 19 lectures that will guide you through the process from beginning to end.
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- Learn how to develop applications for autoscale
- Prepare for the Azure AZ-300 certification
- Design and Implement code that addresses singleton application instances
This course is recommended for:
- IT Professionals preparing for Azure certification
- IT Professionals that need to develop applications that can autoscale
There are no prior requirements necessary in order to do this training course, although an understanding of MS Azure will prove helpful
Microsoft Azure offers built-in autoscaling for most compute features. Such features include Virtual Machines, Service Fabric, the Azure App Service, Azure Cloud Services, and Azure Functions. Autoscaling support for virtual machines in Azure is provided through VM scale sets. VM scale sets are used to manage a set of Azure VM's as a single group. VM scale sets also support autoscaling for service fabric. Because each note type in a service fabric cluster is configured as a separate VM scale set, each node type can be scaled in or out, independent of the other node types. Autoscaling is built into the Azure app service, as well.
As such, autoscale settings apply to all applications within an app service. Azure cloud services offers built-in autoscaling at the role level. All these Azure compute options leverage Azure monitor autoscale in order to provide a common set of autoscale functionality. It's important to note that Azure functions differs slightly from these other compute options. Azure functions differs because autoscale rules do not need to be configured manually. Azure functions, instead, automatically allocates compute power whenever code is running. It scales out as necessary when it needs to handle additional load. Custom autoscaling solutions can also sometimes be useful. By using Azure diagnostics, app-based metrics, and some custom code, you can explore an application's metrics. With this information, you could then define custom rules based on those metrics and use resource manager rest APIs to trigger autoscaling.
It's important to note, however, that custom solutions are not easy to implement. They should only be considered if none of the other built-in approaches works for your requirements. If the built-in autoscaling features of Azure work for you, use them. If they do not, you should carefully consider whether you really need scaling features that are more complex than what is offered out of the box.
Tom is a 25+ year veteran of the IT industry, having worked in environments as large as 40k seats and as small as 50 seats. Throughout the course of a long an interesting career, he has built an in-depth skillset that spans numerous IT disciplines. Tom has designed and architected small, large, and global IT solutions.
In addition to the Cloud Platform and Infrastructure MCSE certification, Tom also carries several other Microsoft certifications. His ability to see things from a strategic perspective allows Tom to architect solutions that closely align with business needs.
In his spare time, Tom enjoys camping, fishing, and playing poker.