Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

VMware vCenter Server, the centralized management point in vSphere, is used for managing ESXi hosts, clusters, VMs, and other components in your virtualized data center. This blog post addresses the 503 Service Unavailable that you may get in vSphere Client when you try to connect to vCenter. Read to learn about the potential causes of this error and how to fix it. NAKIVO for VMware vSphere Backup Complete data protection for VMware vSphere VMs and instant recovery options.

Pharmaceutical regulation relies on three core pillars: Maximum system availability, trustworthy data and rapid recoverability. With the right strategy, manufacturers can uphold them all. Operational technology (OT) systems such as SCADA, manufacturing execution systems, cleanroom controls, environmental monitors and laboratory automation are essential for maintaining validated, compliant and uninterrupted production. When those systems fail, downtime can result in enormous financial costs.

DevOps engineers running OpenShift know the platform well. They know how to build on it, scale on it, and operate it under pressure. What they often hit unexpectedly is the question of backup and recovery, especially once OpenShift Virtualization enters the picture. Most of the tooling that exists today wasn’t built with Kubernetes in mind. It was built for something else and extended toward it.

Growing firms migrate between Git-based platforms to provide the capabilities they need. The goal is to secure repos while integrating them with various tools. It may sound trivial, yet repos and DevOps platforms are inherent to business growth and evolution. A typical example may be moving repos from Bitbucket to GitHub. If so, let’s consider why and how you can migrate your repositories from Atlassian to a Microsoft-owned solution. Table of contents: hide Why migrate from Bitbucket to GitHub?

Governments and regulated enterprises are pulling their most sensitive workloads out of infrastructure they can’t fully control. That’s the core driver behind sovereign cloud: cloud infrastructure where data residency, jurisdictional control, and supply-chain transparency are architectural requirements, not optional features. With GAIA-X moving into implementation and vendors like Red Hat launching sovereign support models for EU member states, adoption is accelerating fast.

If managed service providers (MSPs) are going to grow as 2026 rolls on, they’re going to have to overcome both new and familiar obstacles in a tough environment. But there is good news for MSPs that are ready to adapt their business models to new market realities. A recent report from Omdia, MSP Trends and Predictions 2026, lays out clearly why MSPs are more likely to struggle to grow in 2026 than they have in past years.

We’re excited to announce that GitProtect, an enterprise DevOps Backup & Disaster Recovery software, is now officially available on Microsoft Marketplace! This milestone represents more than a new distribution channel. It reinforces our commitment to delivering secure, enterprise-ready DevOps data protection, which is now also accessible through a trusted Microsoft ecosystem.

WORM (Write Once, Read Many) is a data storage model specifically designed to guarantee data integrity over time. In a WORM-compliant storage, data is written once and cannot be altered or erased for a defined retention period (can be read as often as needed though). Table of contents: hide What is WORM (Write Once Read Many) How WORM works in practice WORM vs immutable storage Why WORM is important against ransomware WORM-compliant storage in GitProtect Why WORM alone is not enough.

Vector databases are everywhere now. If you are building anything with AI—recommendation engines, semantic search, RAG pipelines—you are probably running a vector database. And if you are running it in production, you are running Milvus on Kubernetes. Here is the problem. Your vector database holds millions of embeddings. Maybe hundreds of millions. Each one represents expensive processing—API calls to OpenAI, inference from your own models, hours of batch jobs.