Real User Monitoring (RUM) is the crucial element of the resolution process for any application engineer facing a grave “user-experience” bug thats taking place on any web browser. Telemetry is collected by RUM regarding the application. From the end user perspective, this is done to provide guidance for the engineer in understanding critical aspects of the user journey.
By taking various steps, such as switching between different products to identify the applicable team and gathering additional information from them, one can acquire the needed information. Connecting data from APM, RUM and removing barriers between teams is an effective way to rectify customer experience deterioration, unresolved errors, and lost revenue.
Real User Monitoring (RUM)
RUM collects and processes essential details such as page loading time, location of the user, type of device, the browser, the OS, version of application, the URL and even errors/crashes. This helps provide a committed UI which makes the data easily accessible. However, correlations between frontend data collected by RUM and backend information are necessary to find a potential solution – this is the decisive part missing from the equation.
What APM tracks?
APM provides visibility into the lifespans of individual requests going through an application, which can be beneficial to engineers as they try to understand how an application interacts with its supporting infrastructure. APM products contain distributed tracing capabilities that allow for surface-level RUM tracing during troubleshooting.
APM & RUM Correlation
By utilising APM as well as RUM, and making the data easily accessible between them, engineers can reap the best possible results. With RUM tool providing information on spikes in latencies and errors across various micro-services, having this data conveniently correlated allows for faster issue resolution.
APM & RUM Use Cases
The combination of RUM and AOM can bring more than just better collaboration and improved conflict resolution. If done correctly, this merged data enables Machine Learning usage. This allows one to detect the effects that backend problems have on the business. When comprehensive frontend as well as backend information is unified in one system, the alerting framework (ML-based) has complete data to work with. It also becomes increasingly accurate with time in determining causes for issues within an environment. By correlating these results with financial metrics, it is possible to trace outages back to their root cause in terms of a backend incident. These capabilities enable the joint data of RUM and APM to have extremely powerful implications to understand how the interaction between a business and the infrastructure. It also improves root-cause analysis via Machine Learning techniques.
Best Business Results
Completely correlated RUM and APM give the best business results because of lesser errors and those errors can be rectified quickly. Time saved for troubleshooting can be effectively used for creating new things and this is what makes innovation and business success.
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