How Big Data And Predictive Analytics Fit Perfectly with APM
October 17, 2019

Navin Israni

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Do you want to be absolutely certain about what experience your application is delivering to your users? Do you want to quantify your app’s performance?

Application Performance Management (APM) is a set of tools that helps businesses monitor the application’s performance in terms of its capacity and levels of service. APM tools measure the application’s performance by monitoring all of its subsystems — the servers, the virtualization layers, the dependencies, and its components. 

As the data generated by organizations grows, APM tools are now required to do a lot more than basic monitoring of metrics. Modern data is often raw and unstructured and requires more advanced methods of analysis. The tools must help dig deep into this data for both forensic analysis and predictive analysis.

To extract more accurate and cheaper insights, modern APM tools use Big Data techniques to store, access, and analyze the multi-dimensional data.

The first advantage of using Big Data is that agents can simply "look at" insights without having to derive them with experiments or data sampling. Because data from multiple "infrastructure universes" is available on the dashboard, it also saves their time usually spent in running queries and testing assumptions.

Big Data can be useful in many more ways.

1. The analysis it presents is definite, not circumstantial

If the team spends time on testing based on prior experience and the analysis fails to confirm that assumption, it is simply a waste of time. When conducting a root-cause analysis of any issue, it’s important to eliminate scenarios to avoid going in the wrong direction. 

However, because Big Data analyzes all possible data sources, agents don’t miss any data. They definitively discard faulty assumptions without having to test them.

With the help of Big Data supporting tools, admins can identify unique signatures of attacks by analyzing data from several tools across all architectural layers.

2. Diagnosis of intermittent and user-triggered errors gets easier

The back-end environment of the application may not be fully apparent when major errors must be resolved intermittently. It’s also hard to predict when these errors will recur. Moreover, observing the progression of these errors becomes difficult as these environments undergo evolution over time.

Big Data helps extract valuable insights to help improve the user experience. This makes user experience analytics one of the most attractive benefits of Big Data-powered apps.

With a Big Data approach, ops teams can diagnose quickly as these tools capture data continuously. This makes the life of diagnosis teams much simpler as all the forensic data is available regardless of the state of the environment or the timing of the problem.

When the source of the error is a user action, an APM tool that integrates Big Data will swoop in and capture a snapshot of all the components in all layers of the environment. This allows agents to trace the action directly and definitively to the exact problem.

3. Error prediction improves the quality of the app

Quality assurance is one of the most important aspects of creating web apps. Whether you are using an app builder or hiring a custom agency, errors can still occur long after you have developed and deployed the app.

Therefore, you have to plan for QA tasks during the maintenance phase of the application too. 

In this phase, often the agents would simply focus on solving problems that do occur. That means they may ignore any signs of future problems; they let the wounds fester without doing anything about it. 

But can agents proactively find out problems?

APM Big Data helps do just that. With the highly detailed environment data, agents can spot anomalies and take actions to fix them before they result in massive errors. 

Unforeseen use cases can be studied with the certainty of rich, actionable insights as they happen. Robust apps that catch all possible errors are hard to create in the first release. But with APM and Big Data Analytics, patterns can be found to predict future errors and take proactive steps to prevent them.

Final Words

There is no fixed amount that qualifies a dataset for Big Data applications. However, complex unstructured data being used for innovative and unconventional applications definitely makes the APM technology a candidate for Big Data. 

Big Data technologies can analyze data from not just the app’s infrastructure, but also provide a complete and instantaneous snapshot of its ecosystem as well.

Navin Israni is a Senior Content Writer at Arkenea
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