Technology / DevOps

3 Tips for Designing a Logging and Monitoring Solution on Azure

3 Tips for Designing a Logging Solution on Azure
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Published on February 16, 2023

Are you tired of digging through heaps of data to figure out why your Azure compute resources are failing? Or is your boss pestering you to create an early warning system for your Azure subscriptions?

Did you land on this article because you need to study for the Microsoft AZ-305 exam? Whatever your reasoning for reading this post, we have three amazing hot tips for designing a logging and monitoring solution on Azure. 

(Pssst … if you need help studying for the AZ-305 exam to earn the Azure Solutions Architect Expert certification, check out Knox Hutchinson’s course at CBT Nuggets.)

Ready to Build Monitoring and Logging Solutions in Azure? 

These tips will help you start creating monitoring solutions in Azure, but if you want to take it a step further, I recommend Knox Hutchinson’s Azure Solutions Architect Expert AZ-305 online course. Mr. Hutchinson does a deep dive into Azure services. He covers topics like data governance and authentication, data ingestion and integration, disaster recovery, and utilizing Azure for high availability.

As a bonus, you’ll learn everything you need to prepare for the AZ-305 Microsoft exam. Take the course

Not a CBT Nuggets subscriber? Sign up for a one-week no-strings-attached trial to check out this course and others!

Configure Intelligent Alerts in Azure

Azure’s monitoring services include robust alerting tools. These alerting tools can integrate with both Azure Metrics and Azure Logs. By default, most Azure services integrate with Metrics and Logs, and that data can be piped to the Alerts product. 

Alerts are helpful for all sorts of things. For example, if a web server is powered by an Azure-hosted VM, and that web server is flooded with traffic and spikes CPU, memory, or network traffic usage, Alerts can send real-time notifications to the cloud ops team for investigation. 

Likewise, Alerts can be configured to push billing notifications. Systems administrators have posted many horror stories online about incurring enormous fees from cloud service providers. Rogue Google or Facebook bots got stuck looping through their website. Those bots pushed tens of thousands of requests to their API, pushing their egress traffic (and, as such, costs) through the roof. Those bots accidentally simulated a DDoS attack, and the businesses paid for it. 

Make sure to configure alerts for every possible doomsday scenario in Azure. They only take a few minutes to configure. 

Simplify Cross-Department Communication with Power BI

You can take full control of your data analytics solution at the largest possible scale using Microsoft Power BI and tools available in Microsoft Azure. An added bonus: Management loves it!

One of the coolest features in Azure monitoring services is Workbooks, which is a reporting tool that uses complex custom configurations (pre-defined or self-defined complex telemetry queries). Workbooks data can also be piped into Power BI. 

It’s not management’s responsibility to understand technical language and concepts; that responsibility falls on IT. Sometimes cloud solutions architects need help conveying the technical information to nontechnical stakeholders.

Piping data into Power BI dashboards solves two issues:

  1. Power BI helps explain technical data and trends to nontechnical team members.

  2. Power BI gives agency to nontechnical team members, helping them to understand IT operations and technical debt without requiring lectures from IT staff. That way, everyone stays on the same page. 

Related: How Difficult is the AZ-305 Exam?

Secure Your Cloud with Azure Data Explorer

You can leverage Azure AI for security, specifically, Microsoft's Data Explorer products in Azure, which has access to Microsoft’s ML platform.

One of the core use cases for Azure Data Explorer is data discovery. It can ingest large data repositories (structured or unstructured), analyze them and spit out human-readable content. The ML models that Data Explorer uses are trainable, so security-related data can be piped into Data Explorer for analysis and raise alerts if an anomaly is found. 


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