As your tech stack increases, every new device (network devices, servers, applications) creates a large amount of distributed log data. This forms part of what is called “machine data”, which is growing 50x faster than traditional business data. In fact, everything in your stack is continuously writing new events to your log files, including error logs that contain a record of critical errors encountered by a running application, operating system, or server.

Observability is made up of metrics, logs, and traces. These pillars help us understand the behavior of applications under normal execution, which further accelerates identifying anomalies in case of application failure or deviation from normal execution. Logging is not about tracing each and every operation, it is about sensible, consistent, and machine-readable log messages that expose the application behavior.

In 1927, the world was introduced to the origins of Artificial Intelligence (AI) in the form of a robot in the movie Metropolis. Throughout nearly a century since then, movies have continued to iterate on the complexities of AI, as both a fun take on it and serious commentary on the potential concerns and consequences. This is all well and good, but as AI has continued to evolve, we find ourselves asking, “how can we actually use this to make our lives easier?”

Technology trends transform human behavior permanently. In the past decade, we have improved as a society by embracing digital lives that drive faster collaboration and automation and save us a significant amount of time. The IT Operations landscape is not any different, and artificial intelligence (AI) is at the forefront of that.

If you’re reading this article, you’re most likely looking for a simple one-stop-shop way to understand logs. I’m sorry to be the one to tell you this, but logs are not simple enough to deal with easily. In fact, as you start approaching this topic on a practical level you’ll quickly realize how complex and annoying it truly is.

A key topic of conversation that comes up again and again with our customers is the challenge of collaboration in a remote work environment. Too many channels of communication or documentation are ineffective, and IT professionals are starting to feel fatigued by never feeling quite “in the know” with business decisions that are happening in real-time. When separated from colleagues, teams can feel distant and unmotivated or find it hard to stay focused.

Effective log analysis can help you significantly reduce the time spent investigating and troubleshooting incidents. With the many different log analysis platforms available, it can be overwhelming to choose and difficult to know what to look for. In this short guide, we’ll share the top three things you should consider when selecting a log analysis platform for your business.

LogicMonitor is pleased to announce enhancements to our Slack integration. The new integration is now bidirectional, allowing users to acknowledge and SDT alerts from their Slack workspace. LogicMonitor accounts come pre-configured to integrate with Slack. The integration enables LogicMonitor to create Slack Channel Notifications based on LogicMonitor alerts.

Monitoring has never been simple, but there was a time when it was simpler. You had a device you could collect data from; you knew the metrics you needed to monitor, and if something went wrong, you could find the root cause. But as IT becomes increasingly and exponentially more complex, more devices, more environments, more things to monitor, more updates, more data, more everything; monitoring in general needs to grow with it.

We are pleased to announce another integration with one of our strategic partners, ServiceNow. Available today with the launch of ServiceNow’s Paris release, LogicMonitor supports the Service Graph Connector! LogicMonitor was selected by ServiceNow as a key player and one of the first monitoring platforms invited to this new, exclusive program.