Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

July 10, 2025: Today, Canonical announced the release of Charmed Feast, an enterprise solution for feature management with seamless integration with Charmed Kubeflow, Canonical’s distribution of the popular open source MLOps platform. Charmed Feast provides the full breadth of the upstream Feast capabilities, adding multi-cloud capabilities, and comprehensive support.

We are thrilled to announce that, for the fifth consecutive year, Datadog has been named a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms. We believe that this recognition reflects our continued focus on helping customers observe, secure, and act on everything that matters across their technology stack.

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.

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.

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 global cyber threats continue to evolve at unprecedented speed, the United States public sector faces growing pressure to enhance operational readiness. Agencies must now contend with adversaries who are not only well-funded but also increasingly sophisticated in their ability to exploit visibility gaps. In the face of this dynamic threat landscape, the Zero Trust Architecture (ZTA) model has become an essential security framework.

Mid-sized engineering teams can scale DevOps by embracing automation, optimizing workflows, and fostering a culture of self-service and shared ownership. Learn how process improvements, skill growth, and tools like Qovery enable faster, more efficient delivery at scale.