Monitoring has never been simple, but there was a time when it was simpler. You had a device you could collect data from; you knew the metrics you needed to monitor, and if something went wrong, you could find the root cause. But as IT becomes increasingly and exponentially more complex, more devices, more environments, more things to monitor, more updates, more data, more everything; monitoring in general needs to grow with it.
Enter observability, the answer to simplifying the mounting complexity of IT. But what makes observability so different from monitoring? Is it just the industry buzzword for monitoring 2.0 or is it an actual shift in how IT teams operate in the modern age of technology? Monitoring creates visibility into environments and is a foundational piece of IT operations, but visibility alone doesn’t equal total observability. Logging and machine learning need to be added to make a platform observable. In short, monitoring is one key function of observability, but observability has additional components that ensure teams can move from reactive problem solving to proactive operations.
In this blog, we’ll discuss:
- What is observability?
- What are the key differences between monitoring and observability platforms?
- What are the benefits of observability?
- How do you transform from monitoring to observability?
What Is Observability?
Observability in IT refers to a system’s ability to diagnose what’s happening inside a system or process by observing the external visible information available. This is done by combining monitoring, log analysis, and machine learning to create an environment that can easily detect issues, identify anomalies proactively, and scale with your growth.
The first tenet of an observable system is monitoring itself. Being observable requires as close to total visibility into an IT environment as possible. To correlate data points, devices, and functions, you need to be able to see everything.
Logs are required to find issues and recognize trends in observable platforms. Having records of every event across a vast array of data is necessary to help know what’s happened previously and quickly find errors when something goes wrong.
To automate correlations between metric data and log data, machine learning can be used to detect, analyze, and provide insight. Thousands of logs can be created every second, across thousands of devices. With machine learning, these logs can be analyzed to find anomalies, errors, and issues quickly, sometimes even before a problem arises.
Observability has become more relevant and necessary to strive for than ever. As technology continues to update and expand at such a rapid pace, having observability braces for the future and empowers teams to better tackle digital transformation initiatives.
What’s the Difference Between Monitoring and Observability?
Monitoring is something you do. Observability is something you have. Generally, when you’re monitoring something, you need to have the insight into exactly what data to monitor. If/when something goes wrong, a good monitoring tool alerts you to what is happening based on what you’re monitoring. We use monitoring to track performance, identify problems and anomalies, find the root cause of issues, and gain insights into physical and cloud environments.
You need to be monitoring to have observability. Monitoring can view the external, logging can see the outputs over time, and with these combined, observability can be achieved by inferring the internal based on the historical.
Think of observability as that insight you need to have on exactly what to monitor. Observability isn’t just a beefed-up term for monitoring. It’s proactive; using logs, machine learning, and causation to create a holistic system that is visible for everyone.
What Are the Benefits of Observability?
Observability benefits greatly from monitoring and logging more metrics. More data can be cumbersome for IT professionals looking manually to solve an issue. Thousands of logs are created every second, and monitoring hundreds to thousands of devices can make finding anomalies manually difficult. LogicMonitor itself ingests 300+ billion metrics every day. With an observable platform, the more data being collected and analyzed, the more likely that data can be correlated and used to pre-empt issues even before they arise.
How Do You Implement Observability?
Observability requires a sophisticated toolset and holistic approach to monitor, analyze, and trace events. Here’s a simplified overview of how to best implement observability:
Start With Centralized Monitoring:
Ensure you have as much visibility into your environment as possible. Monitoring is a foundational piece of observability, and a good monitoring platform should provide insight across all of your environments; whether they are on-premise, in the cloud, or hybrid. The more you monitor, the more data you have, and the more you will be able to streamline troubleshooting and reduce mean time to resolution (MTTR). Monitoring answers the where? of observability, showing teams where issues occur.
That said, It’s important to focus on centralized monitoring so that all of your devices and environments (whether they are switches, routers, serverless, cloud instances, etc) can be quickly diagnosed within the same pane of glass. Having one monitoring platform to see AWS deployments and another to see network devices will slow down the troubleshooting process and won’t provide a full picture of where performance issues are happening.
Going through logs manually is cumbersome, and logging every single event quickly creates millions of lines to parse through. In fact, this problem is just going to get bigger. According to IDC, the “digital universe” is growing 40% every year, but most of us only analyze about 1% of that data. We have all of this data, but we lack the mechanisms to make use of it. That’s why it’s crucial to have a tool that goes beyond rule-based policies for reactive discovery of logs, and rather analyzes all of your logs in real-time to surface key events.
Marrying monitoring and log analysis can help to filter out and sort the worthwhile data (critical log events, anomalies, etc) from the junk; saving tons of time and eliminating manual processes. With real-time log analysis, you can answer why and how something went wrong in your IT environment. This pushes us one step further towards full observability.
Finally, we must answer the question of what to do with all of this information. With log analysis and centralized monitoring, we now know where an issue occurs, how it happened, and why it caused a particular chain of events. By adding in machine learning and taking an algorithmic approach to IT operations, we can automate the exponential growth of data into actionable insights that allow you to do more in less time. These processes can help to streamline IT operations by setting dynamic thresholds, identifying anomalies, and finding the root cause of an issue. Over time, machine learning and algorithmic IT Operation systems (such as AIOps) can proactively identify issues by understanding what’s normal for your environment and surfacing an alarm before that issue causes downtime. Once you know what to do with all of your metrics and logs, you can hope to achieve full observability.
Observability requires a sophisticated and holistic approach to using a collection of tools to monitor, analyze, and trace events. Once you achieve observability, your team will be able to keep crucial systems and business applications up and available, enabling your business to innovate and advance forward. To learn more about other industry topics, check out LogicMonitor’s resource page.
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James Yaria is an employee at LogicMonitor.
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