Operations | Monitoring | ITSM | DevOps | Cloud

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.

OpenTelemetry is rapidly becoming the backbone of modern observability, but true innovation happens when you build directly on its latest capabilities. For Launch Week 4.0, we’re excited to showcase five powerful features; each crafted to help you get more value from your telemetry, make debugging faster, and deliver a unified observability experience. Here’s a quick look at what’s new, why it matters, and how SigNoz is pushing the boundaries of what’s possible with OTel.

The Grafanaverse just got a little bit bigger. Today at its annual Build conference, Microsoft introduced Azure Monitor dashboards with Grafana, a new service that provides Azure users with Grafana dashboards natively integrated in the Azure Portal at no additional cost and with little administrative overhead required.

Effective Node.js monitoring requires tracking runtime metrics (memory, CPU), application metrics (request rates, response times), and business metrics (user actions, conversion rates). This guide covers what to track, how to collect it, and how to set up meaningful alerts.

Effective microservices logging requires standardized formats, centralized storage, correlation IDs, and strong security measures. This guide walks you through setup strategies, best practices, and tool recommendations that won't break your budget.