Splunk is a fantastic platform for ingesting, storing, searching and analysing data from logs, metrics and traces from a massive variety of sources. But does that mean we should ignore all of that data that doesn’t fall into these categories, like image and video data for example? Of course not!

I drive a 2005 Ford diesel pickup truck. Most of the time my truck runs great. But occasionally an orange light on the dashboard will flicker on to alert me that something is wrong. Unfortunately, there’s no information about what is wrong and why. My truck has a monitoring solution, but not an observability solution. In many cases, IT has the same problem as my truck.

One of the more delicate debates in the DevOps world is what observability has to do with monitoring. Is observability just a trendy buzzword that means the same thing as monitoring? Is observability an improved version of monitoring? Are monitoring and observability different types of processes that solve different problems? The answer to those questions depends in part on your perspective.

With almost every blog you read about monitoring, troubleshooting, or more recently, the observability of modern application stacks, you’ve probably read a statement saying that complexity is growing as a demand for more elasticity increases which makes management of these applications increasingly difficult. This blog will be no exception, but there’s a good reason for that: we just enabled the first Sumo Logic customers with powerful new tools to tackle these exact challenges.

Cloud services make the daily tasks of business easier. They enable remote workforce collaboration, streamline administrative tasks, and reduce capital costs. However, these “pros” come with a few “cons.” The IT stack’s increased complexity means staff work across divergent log management tools when something breaks. Centralized log management for the cloud makes root cause analysis easier by aggregating all event log data in a single location.

We built LogDNA around the idea that developers are more productive when they have access to all of the logs they need, when they need them. However, we also know that log management can get expensive fast. And, for anyone who owns the budget for developer tools, logs can be an unpredictable line item as you try to determine your monthly, quarterly or even annual spend.

Any organization with data assets is a possible target for an attacker. Hackers use various forms of advanced cyberattack techniques to obtain valuable company data; in fact, a study by the University of Maryland showed that a cyberattack takes place every 39 seconds, or 2,244 times a day on average. This number has increased exponentially since the COVID-19 pandemic forced most employees to work remotely, and drastically increased the attack surface of organizations around the world.

It’s been almost a year since I shared some thoughts about distributed tracing adoption strategies on this blog. We have discussed how different approaches between log vendors and application performance management (APM) vendors exist in the market and how important that is to allow users to analyze the data, including custom telemetry, the way they want.

LogDNA recently celebrated 5 years since our launch in Y Combinator and during this half-a-decade we’ve learned several lessons about balancing cost and scalability. As a founder, here are the top 3 things I wish someone had told me as we were racing towards success. The appeal of building a cloud-native application for a startup is a no brainer—it’s agile, scalable, and can be managed by a distributed team. Not to mention, it’s the cheapest way to get off the ground.

If you’re investigating an incident, every minute means degraded performance or even downtime for customers. The causes of an issue often come from parts of your systems and applications that you would not think to check, and the sooner you can bring these to light, the better.