Operations | Monitoring | ITSM | DevOps | Cloud

We cut log-query scanning from ~100% of data blocks to < 1% by reorganizing how logs are stored in ClickHouse. Instead of relying on bloom-filter skip indexes, they generate a deterministic “resource fingerprint” (hash of cluster + namespace + pod, etc.) for every log source and sort the table by this fingerprint in the primary-key ORDER BY clause. This packs logs from the same pod/service contiguously, letting ClickHouse’s sparse primary-key index skip irrelevant blocks.

As systems move to microservices, serverless, and multi-cloud setups, debugging gets harder. You’re no longer dealing with a single log file; you’re looking at logs from dozens of services, running across different environments. Traditional debugging methods like SSH-ing into servers or adding print statements don’t scale in these environments. Cloud log management tools help by collecting logs from all your services into one place.

You're building a classification model, and your framework throws around terms like "log loss" and "cross-entropy loss." Are they the same thing? When should you use binary cross-entropy versus categorical cross-entropy? What about focal loss? This blog breaks down these loss functions with practical examples and real-world implementations.

If there’s one thing we’ve learned from years of working with innovators across industries, it’s this: Progress happens when people come together—not just to showcase, but to share, ask questions, challenge one another, push both themselves and the industry to the next level, and co-create.

Improper artifact integrity validation is a critical vulnerability in CI/CD pipelines characterised by insufficient mechanisms to cryptographically verify the authenticity and integrity of code and build artifacts traversing the pipeline. When these controls are weak or absent, adversaries with access to any pipeline stage can inject malicious or tampered artifacts that appear legitimate, enabling undetected propagation through the pipeline and eventual deployment into production environments.

One of the most common challenges in modern software engineering today is understanding how requests flow through applications. As system architectures shift to favor widely distributed, cloud-native designs, keeping track of how an application processes user actions is more difficult than ever. A single user action may trigger events processed in dozens of backend services. Traces are helping software developers today with this challenge.

The conversation around ITOps vs DevOps continues as organizations pursue agile development and responsive service delivery. While both practices share the goal of improving software and infrastructure management, they emerge from distinct historical, operational, and cultural backgrounds. Understanding how these models differ at their core helps decision-makers choose the most suitable operating strategy and align their teams for smoother collaboration.

Let's be honest: most conversations about AI ethics sound like they're happening in a boardroom, not an ops room. But here's the thing, when you're using AI to make sense of your telemetry data, ethics isn't some abstract concept. It's the difference between insights you can trust and algorithmic noise that leads you down the wrong path. The uncomfortable reality? Your AI is only as ethical as the messiest, most biased piece of telemetry data you feed it. And if you think your data is clean, well...