Operations | Monitoring | ITSM | DevOps | Cloud

Traditionally, companies had to use separate tools for ETL, data storage, and analytics. Often, this resulted in slow, complex, and expensive data workflows A good example is PwC’s Deals, Insights & Analytics (DIA) team, which faced similar challenges. According to Microsoft, bespoke solutions often took months to build and were difficult to merge, slowing projects and driving up costs. That changed when PwC adopted Azure Synapse Analytics. The result?

If you’re like most developers, you know that writing code isn’t the bottleneck anymore. AI has made it faster than ever, and chances are you’re already using it. Yet, delivering software is still complex because of everything else you have to manage: fixing vulnerabilities, reducing tech debt, cleaning up feature flags, ensuring test coverage, writing documentation, and the list goes on. That’s why we built Rovo Dev, a context-aware AI agent for developers.

This post is a deep dive into how we improved the P95 latency of an API endpoint from 5s to 0.3s using a niche little computer science trick called a bloom filter. We’ll cover why the endpoint was slow, the options we considered to make it fast and how we decided between them, and how it all works under the hood.

Atlassian is shutting down OpsGenie. New sales already stopped on June 4, 2025, and the platform will be completely offline by April 5, 2027. As an OpsGenie user, you now face a critical decision: Migrate to Jira Service Management (JSM), Atlassian’s recommended path, or choose a different solution. And if you’re not sure JSM is the right fit for your team’s incident management needs, this review will help you decide. I signed up for JSM and put it through real-world testing.

When you run Kubernetes at scale, one of the first challenges is understanding what the cluster is actually doing. Workloads shift around, pods restart for normal reasons, and traffic doesn't always follow the patterns you expect. Having clear signals makes day-to-day operations much easier. That's where kube-prometheus-stack helps. It brings Prometheus, Grafana, Alertmanager, and supporting components together as a single package.

When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, the AI landscape is ever-evolving. The challenge for today’s enterprise is already evolving beyond simply managing a library of known models.

A fundamental shift in software development is already here. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants – a massive leap from less than 10% in early 2023. While this AI-driven speed creates a competitive advantage, it also opens a dangerous new front in the battle for software supply chain security.

Sylva 1.5 becomes the first release to include Kubernetes 1.32, bringing the latest open source cloud-native capabilities to the European telecommunications industry With the launch of Sylva 1.5, Canonical Kubernetes is now officially part of the project’s reference architecture. This follows its earlier availability as a technology preview in Sylva 1.4.