Operations | Monitoring | ITSM | DevOps | Cloud

Inconsistent SQL formatting is a silent productivity killer. The database will execute it, but for developers, poorly structured queries lead to slower reviews, harder debugging, and errors that slip through unnoticed. Over time, this lack of consistency compounds into costly technical debt. This guide shows how to format SQL code so it remains clear, consistent, and easy to maintain.

Service desk AI has become one of the hottest topics in IT. Everyone wants to slash ticket volumes, resolve incidents faster, and give employees the kind of instant, self-service support they expect in the modern workplace. The appeal here is obvious: fewer tickets, happier users, and IT teams finally freed from the grind of repetitive, reactive firefighting.

Modern data centers depend on multiple teams, each using their own systems—CMDBs, ticketing platforms, cloud and virtualization tools, network and server management software, observability stacks, collaboration apps, and countless spreadsheets. Each tool provides important insights, but together they create a complex and sprawling technology landscape.

Logstash has been a workhorse in data processing pipelines for years, but it was not designed with today’s security operations in mind. Security teams now deal with massive telemetry volumes, rising SIEM costs, and diverse log formats that require constant normalization. In this environment, Logstash shows its age: manual configuration, outdated parsing, and scalability bottlenecks introduce fragility instead of efficiency.

In Kubernetes, applications are constantly changing — new pods start, old ones shut down, workloads shift across nodes. The challenge is making sure that different parts of your system, and even external clients, can still find each other when the actual locations keep moving. That’s what service discovery handles. It provides a stable way for applications to connect and communicate, no matter where they’re running or how often the underlying infrastructure changes.

When someone leaves your organisation — whether they resign, retire, or are let go — it’s easy to think the hard work is over. But the moment an employee’s last day arrives, a new risk window opens. If their access isn’t revoked properly or their data isn’t captured, organisations face security breaches, data loss, compliance issues, and rising costs. This is why a well-designed user off-boarding process is just as important as onboarding.

The popular narrative around AI economics is changing. At one time, Moore’s Law conditioned us to expect that smarter, faster computing would steadily get cheaper. When it comes to AI, that expectation holds true at the unit level. Per-token costs are indeed declining. But the number of tokens consumed per task is growing exponentially, making total costs spike. The tension here is important: on paper, inference is getting cheaper.