Splunk Observability Cloud includes powerful features which automatically identify patterns within your data to surface trends. The resulting insights tell you why some customers aren’t getting an optimal experience from your application, and how you can improve it. Unlocking these features requires attributes to be included with your application traces. But how do you know which attributes are the most valuable for your application and business?

While many segments of the IT market move quickly, the observability space seems to move at lightning speed. Fueled by open source innovation, observability toolsets and best practices constantly evolve. Sometimes, it can be tough to keep up — and even tougher to know where your own observability strategy stands. That’s the exact challenge we aim to address with our new Observability Journey Maturity Model.

Application performance monitoring (APM) has been around for a long time. Odds are if you’re tasked with overseeing app performance, you’ve had to deal with this technology to get an understanding of your applications and any issues that can arise in the name of troubleshooting. But there’s a new approach you should consider: application observability.

At Honeycomb, we are very concerned about privacy and data sovereignty—it’s something we take very seriously, and in an effort to serve our customers better, we’re thrilled to announce that we now offer data residency in Europe. This new instance will allow Honeycomb customers to store their data in the US, in Europe, or both. Let’s talk about the details.

Developers and SREs choose Microsoft Azure to run their applications because it is a trustworthy world-class cloud platform. It has also proven itself over the years as an extremely powerful and reliable infrastructure for hosting business-critical applications. Elastic Observability offers over 25 out-of-the-box integrations for Microsoft Azure services with more on the way. A full list of Azure integrations can be found in our online documentation.

Glimpse the golden insights Cloudsmith’s logs offer and see how easy it is to pull and analyze them like a pro.

Observability isn't just a buzzword; it's a vital compass guiding us through the maze of system health and performance. As we’ve adopted microservice architectures, the ability to know ‘what is currently happening in our system’ has diminished as our operational resilience has increased. We find services scattered among a maze of interconnections and interdependencies. And even the logs that used to guide are now scattered throughout this maze.

Oren Lion, Director of Software Engineering, Productivity Engineering, and Tim Schruben, Vice President, Logistics Engineering, both work for TeleTracking, an integrated healthcare operations platform provider that is Expanding the Capacity to Care™ by helping health systems optimize access to care, streamline care delivery, and connect transitions of care.

Splunk’s recent update to its Machine Learning Toolkit (MLTK) is a good reason to spend a few paragraphs thinking through the links between Observability and machine learning. First, let us quickly review what Observability and machine learning are.

With a slew of options at hand, choosing the best observability tools is challenging. Not to mention the strenuous task of researching the web, reading reviews, and scheduling demos to find the best option.