Operations | Monitoring | ITSM | DevOps | Cloud

By applying data engineering and machine learning to raw network telemetry, it’s possible to surface insights that would otherwise go unnoticed. Learn how this approach helps teams detect anomalies in real time, forecast capacity needs, and automate responses across complex, multi-domain environments.

IT Ops Problem Solver Series – Part 2: This article is a summary of a full report in our IT Ops Problem Solver Series. In this series, we’ll tackle the biggest problems facing IT Ops leaders and explore how some of Galileo’s clients are addressing them. In this part of the series, we delve into IT performance challenges and how to address them effectively.

SolarWinds is a popular IT infrastructure monitoring tool deployed on-prem, most well-known for its network and server monitoring capabilities. While it offers rich telemetry, it’s easy to miss the bigger picture. SquaredUp turns this complex monitoring data into clear, shareable dashboards that make it easier to spot trends, catch issues early, and keep everyone on the same page.

Synthetic monitoring simulates user interactions with applications to proactively identify performance issues before they impact real users. Modern distributed systems require sophisticated monitoring capabilities to effectively test microservices, APIs, and complex user journeys across diverse environments. This article provides a framework to evaluate synthetic monitoring platforms in 2025.

Apache Flink is an open-source, distributed stream processing engine built for real-time, high-throughput data pipelines. It excels at processing continuous data streams with low latency, making it a great fit for use cases like fraud detection, log analytics, real-time dashboards, personalized recommendations, and IoT telemetry.

When was the last time you asked about the architecture behind your observability provider? For most IT professionals whether in development, operations, or security, it’s not a question that naturally comes up. Yet, this architectural detail could be the difference between insight at scale and runaway costs. People are drawn to the features, the shiny things. They promise to unlock insight, drive faster response times, and tighten security.

You shipped your latest release. You tested it on emulators, QA devices, and the latest OS versions. But now it’s live and running on thousands or millions of mobile devices, across a jungle of screen sizes, hardware specs, OS versions, and network conditions. A user reports a crash on an old Samsung device over 3G. Someone else complains the app feels “sluggish” after updating. You dig through logs. Rebuild test cases. Ping the backend team. Try to reproduce. Yet, still no answers.

Hariom Gupta Follow 4 min read· 1 hour ago -- Listen Share Starting this journey was both exciting and fulfilling — and now, here I am at the finish line, having successfully completed the LFX Mentorship Program and reflecting on the experience through this blog. The past three months have been incredible — surpassing my expectations in so many ways.