The latest News and Information on Log Management, Log Analytics and related technologies.
Status pages have become the end-users window into your team’s operations. Companies with status pages are doing the right thing for their users — building in some transparency while mitigating frustration and support contact. For the benefits of status pages to pay off, organizations need to treat them as something more than active wiki-pages run by support.
Observability is a mindset that lets you use data to answer questions about business processes. In short, collecting as much data as possible from the components of your business — including applications and key business metrics — then using an AI-powered tool to help consolidate and make sense of this huge volume of data gives you observability into your business. Having observability for your business and applications lets you make smarter decisions, faster.
Log tracking, trace log, or logging traces… Although these three terms are easy to interchange (the wordplay certainly doesn’t help!), compare tracing vs. logging, and you’ll find they are quite distinct. Logs, traces, and metrics are the three pillars of observability, and they all work together to measure application performance effectively. Let’s first understand what logging is.
With the release of Splunk 9.0 came support for SmartStore in Azure. Previously to achieve this, you’d have to use some form of S3-compliant broker API, but now we can use native Azure APIs. The addition of this capability means that Splunk now offers complete SmartStore support for all three of the big public cloud vendors. This blog will describe a little bit about how it works, and help you set it up yourself.
If you’re new to the concept or just trying to keep up with the conversation, Gartner defines Observability as the evolution of monitoring into a process that offers insight into digital business applications, speeds innovation and enhances customer experience. Some folks think that Observability is a new buzzword, but in fact the term was coined in 1960 by Rudolf E. Kalman, a Hungarian-American engineer.
Have you ever had a tough time debugging your Python code? If yes, learning how to set up logging in Python can help you streamline your debugging workflow. As a beginner programmer, you’ll have likely used the print() statement—to print out certain values across runs of your program—to check if the code is working as expected. Using print() statements to debug could work fine for smaller Python programs.
Tracing, or more specifically distributed tracing or distributed request tracing, is the ability to follow a request through a system, joining the dots between all the individual system calls required to service a particular request. Although tracing logs have been around for some time, the trend toward distributed architectures, microservices, and containerization has elevated it from nice-to-have status to an essential piece of the observability puzzle.
