Operations | Monitoring | ITSM | DevOps | Cloud

Note: As of publication, NVIDIA has not released final specifications for the GB300 NVL72. The details shared here are based on projections informed by GB200 benchmarks, industry analysis, and expected generational improvements. As the AI arms race accelerates, data center professionals are already preparing for what’s next: the anticipated NVIDIA GB300 NVL72.

Microsoft Teams is a ubiquitous presence in workplaces all over the world. Prior to 2020, its usage was relatively moderate, with around 20 million users. However, global restrictions during the pandemic led to a 3,500% growth. Teams is now so central to business operations that Microsoft retired Skype in its favor. But this massive scale created a new problem – businesses needed better ways to monitor and report on their Teams usage.

ElasticSearch is one of the IT and software industry’s most established platforms for storing and analyzing log data. As its name suggests it also has a powerful search and analytics engine based on the ElasticSearch Query language. ElasticSearch itself is essentially a backend store, so if you want to explore and analyze your data, you will need a visualization layer such as SquaredUp and our ElasticSearch PlugIn.

India’s digital economy is on a historic growth trajectory. According to the Digital Infrastructure Providers Association (DIPA), it’s projected to reach $1 trillion by the end of 2025, driven by rising internet penetration, data consumption, and cloud adoption.

This series of articles discusses the development of a SOTA Open Smart Ring － a tiny wearable packed with electronics that fits on your (even the smallest) finger. We dive deep into what it means to develop such a product, its challenges, and ultimately, how to make it a manufacturable and usable piece.

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.

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.

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.