Operations | Monitoring | ITSM | DevOps | Cloud

Let's be provocative for a moment. You probably don't know what is actually on your network. You have the CMDB, spreadsheets, diagrams from the last big refresh, and the institutional knowledge of your veteran engineers. But is this information accurate? Is it complete? Answering that question with absolute certainty can be difficult for many who manage complex IT environments.

Predictive Analytics Models and Algorithms are an important component of eG Enterprise’s AIOps engine for proactive observability. eG Enterprise collects and analyses metrics, events, logs and traces and the data including real usage data is used to make intelligent predictions to forecast future system behavior and IT resource metric levels.

I was thrilled to host our latest tech talk, where we got to do a deep dive into vmanomaly with the best possible guests: Fred Navruzov, the actual team lead for the product, and Co-Host, Matthias Palmersheim. We covered a ton of ground, from high-level concepts to the nitty-gritty of configuration. For everyone who couldn’t make it, I wanted to share my personal recap of the most important technical takeaways from our conversation.

Service Level Objectives (SLOs) are a cornerstone of modern software engineering. Defining alerts around SLOs has become standard practice, but many of the common patterns in use today miss the early signals that can tell a customer before an SLO breach has occurred.

In today’s technological landscape, mobile applications are on the rise, boosting efficiency, portability and accessibility in daily life, across a spectrum of industries, from financial services to food delivery. As mobile apps become more essential, the quality of their features, performance, and user experience is critical.l.

Every system in your environment—cloud, on-prem, or hybrid—generates log files. They capture everything from user actions to system failures, security events, and performance issues. But with so many log types and so much raw data, it’s easy to get buried in noise and miss what matters.

Data is more than a record of what happened—it shapes what happens next. Across industries, connected devices continuously stream time-stamped data that reflects the current state of machines, environments, and systems. This steady flow gives organizations a live view of operations and the ability to catch issues early, adjust quickly, and operate more efficiently. However, capturing data alone does not create value.

When you make a credit card transaction, the last thing you want to think about is your data getting stolen. Fortunately, credit card companies put several measures in place to make sure this doesn’t happen. For businesses dealing with customer payments, PCI DSS compliance measures are a simple and necessary step in making sure customer credit card data is well protected. Ensuring PCI compliance can be a complex undertaking.

PCI DSS is an industry security standard existing primarily to minimize the risk of debit and credit card data being lost. This is in the interest of both the customer and the merchant, because if data is lost or misused, the merchant could be subject to legal action. To protect yourself and your customers, you first need to understand the six PCI DSS control objectives and how to meet them.