Operations | Monitoring | ITSM | DevOps | Cloud

Generative AI has now become an essential tool in reshaping retail as we know it. Major players, including Amazon, Walmart, Carrefour, and more, have integrated GenAI into their operations, unlocking unprecedented efficiency and personalization in their strategies. As AI continues to evolve, it’s no longer just about retail media and advertising—it’s revolutionizing every aspect of the retail landscape, from product development to customer service.

In the dynamic landscape of cloud operations, Network Operations Centers (NOCs) are crucial in ensuring service reliability and performance. However, managing the diverse workloads of NOC teams can be challenging. At MoovingON, we are dedicated to providing solutions that enhance operational efficiency and cultivate a positive work environment for our engineers. To this end, we are excited to introduce a powerful new feature in our moovingon.ai platform: Enhanced Dashboards for Workload Management.

If you're like most companies running large-scale, data- intensive workloads in the cloud, you’ve realized that you have significant quantities of waste in your environment. Smart organizations implement a host of FinOps and other activities to address this waste and the cost it incurs: … and the list goes on. These are infrastructure-level optimizations.

SquaredUp is a data visualization tool that can connect to a variety of data sources to bring the data together in a single pane of glass. In this blog we will demonstrate how SquaredUp can connect to any REST APIs out there and build dashboards on the data returned.

Monitoring the correct metrics is crucial for efficient IT operations, as it ensures the smooth functioning of an organization's infrastructure. One crucial aspect of this process is log management, which empowers IT teams to address critical aspects of IT infrastructure, including performance, availability, security, resource usage, and integration.

All but one of the kosli attest commands calculate the true/false compliance value for you based on their type. For example, kosli attest snyk can read the sarif output file produced by a snyk scan. The one that doesn’t is kosli attest generic which is “type-less”. It can attest anything, but Kosli cannot calculate a true/false compliance value for you. Often the tool you are using can generate the true/false value, which is then easy to capture.

Syslog was developed in the 1980s by Eric Allman as part of the Sendmail project and adopted by many systems over the years. When looking at Syslog, there are a few protocol options, each with slight differences. In this reference guide, I’ll break down the differences so that you have a guide to see these formats when utilizing this protocol.

HAProxy 3.1 makes significant gains in performance and usability, with better capabilities for troubleshooting. In this blog post, we list all of the new features and changes. All these improvements (and more) will be incorporated into HAProxy Enterprise 3.1, releasing Spring 2025. Watch our webinar HAProxy 3.1: Feature Roundup and listen to our experts as we examine new features and updates and participate in the live Q&A.

Most developers are familiar with Opentelemetry core components—Traces, Metrics, and Logs. But there’s one part of the OpenTelemetry ecosystem that doesn’t always get the spotlight: processors. These behind-the-scenes operators shape your data pipeline, helping you filter, enrich, and fine-tune telemetry data before it reaches your backend systems. Processors play a key role in making sure your data is cleaner, more useful, and just the way you need it.

Logs are crucial for understanding what's happening in your system, but they can often be hard to make sense of. Log parsing is the key to turning raw, unstructured data into something useful. In this blog, we'll explore the basics of log parsing, its importance, and how it helps you extract valuable insights from your logs without all the clutter.