Tailored Well-Architected Assessments for your workloads

This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Tech Community.

branching.png

With the Well-Architected Framework, Microsoft has provided the blueprints to empower users with best practice across five pillars—Reliability, Security, Cost Optimization, Operational Excellence, and Performance Efficiency

 

Part of effectively utilizing the Well-Architected Framework is understanding the tradeoffs between these five pillars as well as deciding the level of importance for each pillar on a specific workload.  For example, a pre-production workload may value Cost Optimization over Performance Efficiency. 

 

Another important step of effectively utilizing Well-Architected is to understand the tenets and best practices for the demands of your specific workload.   For example, a web app may require more app service instances to provide fast response times during peak holiday traffic while a compute-heavy workload may require design changes or more horsepower to process requests asynchronously. 

 

Recently we introduced three new workload types into the Well-Architected assessment—Data Management, Machine Learning, and IoT.  Each comprehensive assessment provides specialized guidance based on research and experience specific to each workload type.  In addition, these assessments provide resource-specific guidance based on the actual architectural makeup of your workload including the services, storage, and frameworks that make up the DNA of your workload.

 

Whether your workload is built on SQL Server, Synapse, or other data services, the Data Management assessment will ensure you’re following the specific tenets of all five pillars with guidance specific to each service.

 

Incorporating local sensors, edge devices, and hyperscale cloud into your IoT solution can be challenging and complex to implement. The Azure IoT assessment helps you address these challenges and build a secure, robust IoT solution from edge to cloud.

 

Machine learning (ML) brings special challenges to an architecture. From data security to Responsible ML principles and deployment of sensitive intellectual property, the machine learning assessment helps you ensure your solution is robust along all five Well-Architected pillars.

 

Actionable and specific to your workload type, the Well-Architected Framework empowers you to ensure your workloads are meeting the demands of your organization. 

 

Take a Well-Architected assessment to see how your workloads stack up!

 

 

REMEMBER: these articles are REPUBLISHED. Your best bet to get a reply is to follow the link at the top of the post to the ORIGINAL post! BUT you're more than welcome to start discussions here:

This site uses Akismet to reduce spam. Learn how your comment data is processed.