Running a regular log analysis session is one of the most important tasks any modern business can undertake. Unfortunately, log analysis is seen by some teams as a retrospective activity to root cause an issue. This is a mistake. Regular log analysis is the most efficient ways of identifying behavioral issues in systems, configuration errors, and software bugs that may have been overlooked in the development process. It’s also a useful preventative exercise to identify administration misuse or intrusive activity, before you experience a wider security incident, or service degradation.

How to build a more efficient log analysis process

In a world of containers and microservices (coupled with ever increasing release cycles), the sheer volume of data being created requires you to have the right tooling in place, and to rethink your log management and analysis processes.

Remember, logs are being generated by just about everything in your stack. From application logs and operating systems, to server logs - your data is highly distributed and often noisy. Not only does this make data analysis complex, but if your existing log analysis tools are billing you on data volumes – it’s also getting expensive!

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Your log analysis will present outliers – a change in frequency, or an unexpected event for example. However, if that change hasn’t tangibly impacted your service, it can be difficult to understand what to do next. Even a seemingly significant change might be passed if your team believes it was an isolated incident.

Two: Learn to be proactive

Your log analysis will present outliers – a change in frequency, or an unexpected event for example. However, if that change hasn’t tangibly impacted your service, it can be difficult to understand what to do next. Even a seemingly significant change might be passed if your team believes it was an isolated incident.