Operations | Monitoring | ITSM | DevOps | Cloud

With NIS2 and DORA now in effect, EU organizations face a fundamental shift in how they approach security. Compliance is a standard built into every layer of your environment, from your hardware and OS to your software configuration. HAProxy alone doesn't make an organization compliant, yet it serves as a critical technical component of a strong security strategy.

Using live data outside production is one of the fastest ways to create compliance risk, because it quickly becomes harder to control who can access it, how it is handled, and how long it is kept. A Test Data Management (TDM) approach provides exactly the kind of controls SOC 2 auditors look for in this situation: an automated, traceable end-to-end process for protecting, provisioning, and removing customer data so it can be used safely in non-production environments.

Stop overpaying for Kubernetes observability. Learn how in-cluster monitoring and AI-driven troubleshooting with Qovery Observe can eliminate APM ingestion fees, reduce SRE bottlenecks, and make your cloud costs predictable.

Cloud adoption was supposed to simplify operations. Infrastructure would become programmable, scalability would become elastic, and distributed architectures would enable resilience at global scale. In practice, cloud has delivered extraordinary flexibility, but it has also introduced a level of operational complexity that traditional observability approaches were never designed to handle.

Industry 4.0 is the term used to describe the fourth industrial revolution, a name given to the integration of physical and digital systems, which includes the internet of things (IoT) and artificial intelligence that are transforming a huge number of industries. At a high level, its goal is to create an efficient, automated process for creating products or services that can be adapted quickly and efficiently to changing customer needs.

You spent the last decade obsessing over connectivity. You bought into the hype that ‘data is the new oil.’ You fitted your entire fleet with sensors and built massive dashboards to track everything from battery cell temperature to tire pressure. The mission was simple: Capture every metric. Congratulations, you succeeded. You are now drowning in terabytes of data. But here is the hard truth: Data without action is just expensive noise.

At a certain point, faster delivery stops feeling like progress and starts feeling like risk. When engineering teams scale from 10 to 50+ developers, the volume of infrastructure changes, database schemas, environment variables, and networking rules, no longer grows linearly. It scales exponentially. This is the scaling inflection point where manual governance breaks.

While the race to expand networking infrastructure to support AI applications continues on land, the same expansion is occurring undersea. Subsea cables spanning Earth’s oceans must evolve to keep pace.

Generative AI has the potential to transform workplace productivity – but do organizations know how to deliver on that promise? New research shows that employees who use generative AI tools engage with them up to ten times per day, spending over three hours per week interacting with AI at work. And yet within the same organizations, large groups of employees have never meaningfully engaged with these tools at all.

Most IT organizations want to improve the digital employee experience. But good intentions alone rarely move the needle. The real shift happens when organizations evolve how IT operates. Traditional IT operations are built around reacting to incidents. But ticket-based operations, or operations based on poor data, lack the ability to create truly predictive ways of working.