Operations | Monitoring | ITSM | DevOps | Cloud

Implementing SLAs, SLIs, and SLOs in an observability suite is now business-critical. Over time, a company’s decision-makers can add a burdensome number of KPIs that force servers and other IT assets to devote excessive processing time to business intelligence. Eventually, the burden becomes so great that employees, managers, and executives start to complain about the system’s sluggishness. Developers know that they need to strike a balance between business needs and IT processes.

Sifting through logs in real-time or post-mortem to pinpoint the problem can take hours – and is often like trying to find the needle in the alert/log haystack. Further, keeping the troubleshooting process efficient can be a challenge due to context switching and relying on manual interpretation of events and technology-specific knowledge.

CRUD and REST are two of the most popular concepts in the Application Program Interface (API) industry. REST was made to standardize the HTTP protocol interface between clients and servers and is one of the widely used design styles for web API. On the other hand, CRUD is an acronym used to refer to the four basic operations executed on database applications. Because both work on manipulating databases’ data, it’s easy to see why people have some confusion between them.

The primary goal of OpenTelemetry (OTEL) is to offer vendor-neutral ways of application instrumentation such that customers are able to switch between Telemetry backends. There are three main components of OpenTelemetry: OpenTelemetry SDK, OpenTelemetry API, and OpenTelemetry Collector. In this blog, we will be covering the architecture, deployment, and best practices of consuming an OTEL collector.

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.