Operations | Monitoring | ITSM | DevOps | Cloud

Logging is an important part of every software application. In addition to capturing user activity, well-structured logs can make it easier to debug problems should they occur. But if your application is split up across several different subsystems, collecting and analyzing disparate logs can be a real challenge. Picture this scenario: You work at a startup that uses a CMS managed by a few admins. You also have a standalone front-end application for users to communicate with your platform via an API.

We’ve had a busy week in the Splunk Partnerverse! In addition to the new Splunk Partnerverse Program announced last week, we also shared some significant partnership news with one of our longest-standing partners, Accenture.

Not every organization is - or even wants to be - a Fortune 500. Unfortunately, cybercriminals don’t care how big your company is. In fact, they often look to target small and midsize businesses (SMBs) knowing that they might have fewer security resources. You have the same problems that the big companies have, but you also have less money and people. Using centralized log management can give you the security solution you need, at a price you can afford.

When teams begin to analyze their logs, they almost immediately run into a problem and they’ll need some JSON logging tips to overcome them. Logs are naturally unstructured. This means that if you want to visualize or analyze your logs, you are forced to deal with many potential variations. You can eliminate this problem by logging out invalid JSON and setting the foundation for log-driven observability across your applications.

We’re all familiar with the typical use cases for log management, such as monitoring cloud infrastructures, development environments, and local IT infrastructure. So we thought it would be fun to cover some of the less usual, more wild use cases for log management, just to show that log management tools are more versatile, and more interesting, than they may seem. If any of these use cases look too interesting to ignore, let us know and we can do a full article on them!

Engineering teams hoping to gain full-stack observability into their environment need access to the relevant logs, metrics, and traces generated by their cloud infrastructure and applications. Accessing the relevant data quickly is essential – not just because it is more convenient, but because faster engineers are also business-critical for many organizations.

OpenTelemetry is the recommended path today for instrumenting applications with tracing in a standard, vendor-agnostic and future-proof way. In fact, OpenTelemetry (nicknamed OTEL) encompasses all three pillars of observability: tracing, metrics, and logs. The tracing element of the specification is now stable with the rest following. This is innovative stuff! You can read more on OpenTelemetry and the current release state on this guide.

When you’re planning an application performance monitoring (APM) strategy, collecting metrics from storage services like Amazon S3 may not seem like a priority. After all, part of the point of object storage is that applications can read and write from storage buckets seamlessly, with minimal configuration and overhead. Unlike databases or file systems, storage buckets don’t require complex configurations that could lead to performance issues.

Image via Pixabay As any developer or IT professional will tell you, when systems experience issues, logs are often invaluable. When implemented and leveraged effectively, the data produced by logging can assist DevOps teams in more quickly identifying occurrences of problems within a system. Moreover, they can prove helpful in enabling incident responders to isolate the root cause of the problem efficiently. With that being the case, maximizing the value of log data is vital.