Observability in Azure Using Built-in and Third-Party Tools

It takes many different resources, deployment methods, and tools in Azure to get an application working just the way you want it. But that’s just the beginning. Building observability in Azure brings your application sustainability, increased visibility, and could even lead to a better user experience.

To add observability, there are a lot of different tools from which to choose. Azure has its own suite of built-in tools, but there are also many third-party add-on solutions available. Today, we’ll look at some of the commonalities of these two approaches and how they differ.

Monitoring vs. Observability: What’s the difference?

Before we dig into observability too much, we first need to know what we mean by the term along with its symbiotic compatriot, monitoring. Monitoring and observability are tightly connected. There cannot be observability without monitoring. But what are they, and why are they important?

Monitoring is the act of actively tracking your system and continuously assessing it for any anomalies, flaws, or problems. It also triggers alarms and can even automate a response to fix issues. Using various tools and processes, monitoring measures performance, health, and other data over a period that can then be analyzed and used to make future improvements.

Observability sets your system up for monitoring. In other words, data gathered with monitoring is what makes observability possible. Observability also sets up the system for analysis, allowing us to gain crucial insights into why things are happening as they are. If the focus of monitoring is on when something happens, observability looks at why that something happened. While there are many observability solutions out there, all of them have one common platform: Azure Monitor.

Azure Monitor

Azure Monitor is a comprehensive, built-in solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments. Using this information, we can better understand how your applications are performing and identify issues affecting them and the resources on which they depend.

The following diagram gives a high-level view of Azure Monitor. As you can see, Azure Monitor uses two fundamental types of data: metrics and logs. On the left side are all of the monitoring data sources that populate these data stores. On the right are the various functions that Azure Monitor performs while using the data collected.

Image source

Gathering all the data from the insights and outside Azure into a single workspace is the main strength of Azure Monitor. It’s also a starting point to bring visibility to your infrastructure.

Azure Monitor’s built-in tools make it easy to:

  • Monitor in real-time
  • Collect data from monitored resources
  • Query data with Log Analytics and KQL
  • Build dashboards and views to visualize resource health and behavior
  • Use predefined workbooks to extract and visualize the common pattern scenarios
  • Create alerts and notifications
  • Automate a response

As a bold comparison to the DIKW pyramid, Azure Monitor brings users to knowledge. And sometimes, that’s enough. Azure Monitor provides export API and collaboration with MS and third-party instruments for those who want more from the collected data.

Observability beyond Azure Monitor

Many third-party instruments use Azure Monitor export API to upgrade user experience beyond the level determined by the Azure Cloud. Comparing each tool individually would be daunting. Instead, let’s look at some features to look for in these third-party tools that are most useful for observability.

Using machine learning to predict anomalies in Azure Services provides early notification of critical performance issues. It also correlates issues across hybrid environments with cloud and on-premise components.

Using the second level of abstraction, creating its own Key Performance Indicator enriches the dashboarding capabilities to aggregate and presents the data to show business goals achievement.

This functionality provides context across the full stack. Captures and unifies the dependencies between all observability data to intelligently combine metrics, logs, traces, and user experience data.

Drill down into any Azure Service, component, or performance parameter using robust analytics tools. Get critical business signals, create health reports, and identify the root cause of performance issues across cloud and on-premise applications.

Artificial Intelligence for IT Operations (AIOps) enables automated streaming, log, wire, metric, text, and event data analysis. It even allows for ML-based event correlation to provide proactive alerts. AIOps has become essential for monitoring and managing modern IT environments that are hybrid, dynamic, distributed, and componentized.

Observability in Azure: Final Thoughts

When setting your environment up to be as observable as possible, there are many different tools at your disposal. Azure Monitor is a solid, out-of-the-box place to start should your organization decide to work with Azure Cloud. But it typically isn’t the end of the road. Enriching its functionality using third-party tools provides a high level of understanding of your applications’ health and behavior. Based on your unique needs, there are a lot of different options that bring predictability and machine learning capabilities that ultimately lead to an AIOps instrument.

Original post found here.

Authored by Veliko Ivanov:

As a Senior Cloud Engineer at MentorMate, Veliko works on our Cloud Center of Excellence (CCOE) team. To date, he holds several certifications in both Amazon Web Services and Microsoft Azure. These certifications include AWS Certified Solution Architect Associate, Microsoft Certified: Azure Solutions Architect Expert, Microsoft Certified: DevOps Engineer Expert, Microsoft Certified: Azure Administrator Associate, and Microsoft Certified: Azure Security Engineer Associate.

He is interested in and passionate about security, networking, DevOps culture, and CI/CD pipelines.

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