Operations | Monitoring | ITSM | DevOps | Cloud

Real talk – logging tools aren’t exactly known for being easy to use, manage, or onboard. With the technology landscape continuously evolving, enterprises are often left scrambling to rapidly adapt and conform to new requirements of operating across hybrid infrastructures. It is in these ecosystems that logs are now more crucial than ever in the day-to-day operations of Ops teams.

The growing importance of technology in business success has forced practically all companies to hire competent, experienced IT professionals. As technology ecosystems become increasingly complex, organizations need a broader range of professionals to focus on tasks like product development, troubleshooting, and customer services. SRE and DevOps have emerged as two of the most critical approaches to success.

Kubernetes pods are the smallest deployable units in the Kubernetes platform. Each pod signals a single running process within the system and functions from a node or worker machine within Kubernetes, which may take on a virtual or physical form. Occasionally, Kubernetes pod disruptions may occur within a system, either from voluntary or involuntary causes.

The evolution of machine data and logging in general has shifted multiple times over the last couple of decades. The log began with Unix and was rooted in command line actions like tail or grep. It evolved from system-based logs to application-based logs and eventually became more UI-friendly and readable. Not only has the log itself evolved, but the purpose of the log and audience for the log has morphed over time as well.

When we launched LM Logs in November of 2020, we knew the product would aid in reducing troubleshooting time and identifying root causes to enable a more proactive approach to not only monitoring and planning, but also taking action. After talking with our customers and understanding what they needed in order to accelerate their business transformation, we focused on a few key enhancements in Q3. TL;DR – Lumber Bob highlights the top features: Want the long version?

Modern software development is evolving rapidly, and while the latest innovations allow companies to grow through greater efficiency, there is a cost. Modern architectures are incredibly complex, which can make it challenging to diagnose and rectify performance issues. Once these issues affect customer experience, the consequences can be costly. So, what is the solution? Observability — which provides a visible overview of the big picture.

In this blog series, we are covering application instrumentation steps for distributed tracing with OpenTelemetry standards across multiple languages. Earlier, we covered Java Application Manual Instrumentation for Distributed Traces, Golang Application Instrumentation for Distributed Traces, and DotNet Application Instrumentation for Distributed Traces. Here we are going to cover the instrumentation for NodeJS.

A JSON web token (JWT) is a URL-safe method of transferring claims between two parties. The JWT encodes the claims in JavaScript object notation and optionally provides space for a signature or full encryption. The JWT proposed standard has started to see wider adoption with frameworks like OAuth 2.0 and standards like OpenID connect leveraging JWTs.

LogicMonitor continues to grow its visibility of critical business infrastructure and applications. We acquired Airbrake to help see more of what is happening in a customer’s environment at the code level. Simply integrating Airbrake’s dashboard metrics within LogicMonitor alongside your existing networks, services, and cloud infrastructure enables you to gain access to errors and the context you need to effectively troubleshoot issues impacting your systems and users.

Sogeti, a Managed Service Provider (MSP) that provides tech and engineering resources worldwide, had a crucial IT challenge to solve. The MSP operates in more than 100 locations globally and was using six different monitoring tools to monitor its customers’ environments. It was a classic example of tool sprawl and needing to scale where multiple teams of engineers relied on too many disparate tools to manage their customers’ environments.