At one particular time, a developer would spend a few months building a new feature. Then they’d go through the tedious soul-crushing effort of “integration.” That is, merging their changes into an upstream code repository, which had inevitably changed since they started their work. This task of Integration would often introduce bugs and, in some cases, might even be impossible or irrelevant, leading to months of lost work.

A while ago, we added Metrics to our observability platform so teams could easily see system information right next to their application observability data—no tool or team switching required. So how can teams get the most out of metrics in an observability platform? We’re glad you asked! We had this conversation with experts at Heroku. They’ve successfully blended metrics and observability and understand what is most helpful to know.

At Logz.io, we’re seeing a very fast pace of adoption for Kubernetes–at this point, it’s even outpacing cloud adoption, with companies running on-prem fully adopting Kubernetes in production. Why are companies going in this direction? Kubernetes provides additional layers of abstraction, which helps create business agility and flexibility for deploying critical applications. At the same time, those abstraction layers create additional complexity for observability.

The use of statistics, advanced algorithms and AI/Ml is becoming omnipresent. The benefits are visible in every walk of life, from web searches, to movie and retail recommendations, to auto-completing our emails. Of course, not many anticipated the dramatic entrance of generative AI in the form of ChatGPT for writing college essays and poetry on arcane topics.

Dear Miss O11y, I remember reading quite interesting opinions from you about usage of metrics and traces in an application. Did you elaborate on those points in a blog post somewhere, so I can read your arguments to forge some advice for myself? I must admit that I was quite puzzled by your stance regarding the (un)usefulness of metrics compared to traces in apps in some contexts (debugging).

This article explores the challenges associated with debugging Celery applications and demonstrates how Lightrun’s non-breaking debugging mechanisms simplify the process by enabling real-time debugging in production without changing a single line of code.

Third-party APIs and cloud based software as a service (SaaS) tools have become a cornerstone of modern enterprises. It is essential to monitor log data and optimize API performance. This will ensure that development teams provide the desired advantages to clients and users. To address this challenge, businesses can use an observability pipeline. It is a set of tools and processes that monitor and analyze data from various sources. That includes third-party APIs and SaaS tools.

Seems like everyone is building a ChatGPT thing right now, doesn’t it? Well we are too! Inspired by so many others, we decided to see what AI could do with our simplified analytics and observability data. Turns out, it can do quite a lot. I’m thrilled to share that we’ve shipped our first AI insights chatbot, Professor Sloth.

It’s 5:00 pm on a Friday. You’re wrapping up work, ready to head into the weekend, when one of your high-value customers Slacks you that something’s not right. Requests to their service are randomly timing out and nobody can figure out what’s causing it, so they’re looking to your team for help. You sigh as you know it’s one of those all-hands-on-deck situations, so you dig out your phone and type the "going to miss dinner" text.

In the Technical Validation report, “SolarWinds Observability: Observability and Service Management for Modern Application Architectures,” TechTarget’s Enterprise Strategy Group examined SolarWinds® Observability for its ability to support organizations no matter where they are in their digital transformation journeys. In this Technical Summary, we highlight some of the high-level findings. For the full analysis, you can download the report for free.