For today’s network operations teams, change is a constant. Applications, app delivery chains, software-defined and physical infrastructures, cloud services, and more are in continuous flux. Further, as organizations continue to pursue ever more strategic digital transformation efforts, the pace of this change only accelerates. These days, about the only constant is the demand being placed on network operations teams.

I think we can all agree that, in general, different users have different needs. For instance, we’ve found that developers generally use Artifactory to find, select, and then install packages into their development environment, while administrators tend to use Artifactory for troubleshooting, confirming package operations, and other related analyses.

Handling IT alert data can feel like you’re drowning in information. The average BigPanda customer uses more than 20 observability and monitoring tools. Between system logs and user reports, an overwhelming amount of information is coming from all directions. That’s why normalizing data is such a critical part of IT operations. Data normalization in IT incident management involves putting data from various tools into a standard format.

In the modern, technology-driven world, IT Management serves as the core framework that empowers organizations to manage and optimize their technology resources effectively. From overseeing daily operations to planning for future growth, IT Management is essential in structuring, organizing, and aligning IT functions with business goals. By strategically managing the digital infrastructure, IT Management ensures that organizations remain agile, efficient, and competitive.

Keeping track of all devices connected to your network is a fundamental part of effective IT management. This process, known as network discovery, is essential for identifying each device and verifying that it’s authorized and secure. Through network discovery, administrators gain visibility into the network, allowing them to monitor devices, detect potential vulnerabilities, and support ongoing network maintenance.

Outages are unexpected events that can suddenly stop an organization's operations. Whether it's a network issue, a key application going down, or a system crash, these problems can cause confusion and disrupt work. Teams scramble to identify the problem, while employees are left in the dark, uncertain about the impact or duration of the issue. A lack of real-time communication can lead to frustrated employees, delayed responses, and prolonged recovery times.

In the first post, we introduced system tables and how to use them to inspect your cluster. In this follow up, we’ll explain some techniques to improve the speed of system table queries.

Keeping a Kafka cluster optimized can feel like a balancing act. Every piece—brokers, partitions, producers, and consumers—has to work in harmony, or you’ll start running into bottlenecks. To get Kafka to run smoothly and handle growing traffic loads, balancing load across the system is key. Let’s go over practical load-balancing techniques that can improve Kafka performance, keep everything running efficiently, and prevent data slowdowns from building up.

This month, Fortune.com reported that TikTok’s web scraper — known as Bytespider — is aggressively sucking up content to fuel generative AI models. We noticed the same thing when looking at bot management analytics produced by HAProxy Edge — our global network that we ourselves use to serve traffic for haproxy.com. Some of the numbers we are seeing are fairly shocking, so let’s review the traffic sources and where they originate.

You’ve found some Long Animation Frames (LoAFs) impacting your site, now you need to fix them! LoAFs can make animations feel sluggish, delay user interactions, and generally reduce your site’s responsiveness, all of which contribute to a frustrating experience for users. Fortunately, by analyzing LoAF data and addressing common performance bottlenecks, you can dramatically improve how smoothly your site runs.