Operations | Monitoring | ITSM | DevOps | Cloud

As an organization, you likely have many choices on where to store, analyze, and correlate your data. Those choices may change or iterate over time, so having an easy way to route data is needed. Enter Cribl Stream, which can route your data where it needs to go and save some effort, time, and money. It can help with organizational-wide initiatives like migrations and consolidations but can also help with smaller-scale initiatives and your day-to-day tasks of simply getting data in.

It was only as I started to study information theory that I truly understood how nonsensically the computer worked in Star Trek: The Next Generation. Decades before voice assistants and at a time when only the most basic language parsing existed in practice, the computer on Star Trek could always give you the answer you wanted. No one ever spent any time clicking into multiple windows to find an answer, and the display always gave information that could be easily summarized in words.

Predictive analytics are a powerful tool, enabling organizations to make informed data-driven decisions. These tools are far-reaching and can deliver impactful results, either in the long term, like supply chain management and overall equipment effectiveness, or in the short term, like anomaly detection. Let’s take a look at what predictive analytics are and how to power predictive analytics engines for continued, meaningful insight into your data and operations.

Cloud costs are notoriously hard to predict—trickier than deciphering the emotions of a housecat. Traditional cost management tools leave many companies with a lack of visibility into where their money is going, which holds back engineering teams from making informed savings decisions. These tools also fail to bridge the gap with finance teams, who speak a different language than their developer counterparts.

Processes—the service workloads that run on your infrastructure—are the building blocks of your application, and it’s critical to know how well they operate at every level of the stack. Degraded process performance can lead to downtime for your mission-critical services, resulting in loss of customer trust and potentially impacting revenue for the business.

In our ongoing exploration of Rust and Ubuntu, we delve into an experimental kernel project that leverages these technologies to create new schedulers for Linux. Playing around with CPU scheduling policies has always been a dream for many kernel hackers and OS enthusiasts. However, such material typically remains within the domain of a few core kernel developers with extensive years of experience.

Status pages are one of the best additions to your monitoring that can significantly reduce the number of support tickets or improve the efficiency of your teams and processes. There are multiple benefits hidden in creating a custom status page, so let’s take a look at all of them and how you can implement them immediately.

In today's digital landscape, applications are at the heart of both our personal and professional lives. We've grown accustomed to these applications being perpetually available and responsive. This expectation places a significant burden on the shoulders of developers and operations teams.

Large language models (LLMs) like GPT-4 and LLaMA have created a whole world of possibilities over the past couple of years. It’s heralded a boom in AI tools and applications, and ChatGPT has become a household name seemingly overnight. But this boom wouldn’t be possible without the powerful tools and frameworks created to facilitate this new generation of apps. One of these frameworks is LangChain, which makes it easy to build new apps using existing LLMs.

The Gartner recent report, “Emerging Tech: Top Use Cases for Network Detection and Response”, lays out three primary use case drives, which include: Before we dive deeper into Gartner findings, let’s talk about NDR from a high level.