• Understand what end-to-end monitoring is and how it works
• Learn about the different stages of end-to-end monitoring
Most conversations about monitoring treat monitoring as a very technical process. They also tend to frame monitoring almost as if it exists inside a vacuum – as if you perform monitoring for its own sake rather than to serve a greater good. And they focus on applying monitoring to specific types of instances or resources – a particular application or server, for example – instead of monitoring an environment as a whole.
The concept of end-to-end monitoring changes all of the above. By treating monitoring as a holistic, integrative process whose ultimate goal is to drive positive user experiences, end-to-end monitoring helps teams get more value from monitoring tools and processes.
End-to-end monitoring is an approach to monitoring that focuses on understanding the state of an IT environment as a whole and understanding how the state relates to the user experience.
End-to-end monitoring is different from traditional monitoring in the following ways:
Because of the focus on aligning monitoring data with the end-user experience, end-to-end monitoring is sometimes called digital experience monitoring, or DEM.
End-to-end monitoring doesn’t require different tools or processes than conventional monitoring. The only things that change when you embrace end-to-end monitoring are how you analyze your data and apply the results of your analysis.
Here’s an overview of how end-to-end monitoring typically works.
To perform end-to-end monitoring, you start by collecting data from the various resources in your environment – just as you would with a conventional monitoring strategy.
It helps if you have a monitoring or observability platform that can collect data from diverse resources so that you don’t have to deploy a different monitoring tool or a different type of agent to each resource you want to monitor.
Next, you integrate or correlate those data sets in a way that allows you to understand how a trend or anomaly that impacts one resource in your environment compares to the state of another resource.
Here again, it’s helpful to have a monitoring platform that lets you aggregate data from multiple types of resources in a central location to analyze it efficiently.
Next, you analyze data from your various resources, focusing on understanding how it adds up to represent the state of your environment as a whole.
You might use data from multiple resources to determine, for instance, how network bandwidth patterns impact application performance across different servers. Or, you could analyze how changes over time to total node count in a cluster align with application availability and network performance.
The final step in end-to-end monitoring is to apply the insights you’ve gleaned to enhance the end-user experience.
If you’ve determined that networking issues are undercutting application performance, you know you need to improve your network performance to improve the end-user experience. If you notice insufficient nodes available to keep applications running optimally, you know you should scale up your infrastructure to enhance what your users experience.
Again, the great thing about end-to-end monitoring is that it doesn’t require you to deploy new tools or learn fundamentally new processes.
It’s really just a matter of having a monitoring and observability suite, like Mezmo, formerly known as LogDNA, that can support a holistic data aggregation and analysis process, then leveraging that tool to gain a comprehensive understanding of what your users are experiencing continuously.