Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on DevOps, CI/CD, Automation and related technologies.

Monitoring Oracle ASM with Custom Metrics | The Tony and Tonie Show Ep 49

Even small Oracle ASM issues can become big database problems. Here's how to spot the warning signs early. Tony and Tonie discuss how Redgate Monitor custom metrics help teams close a common monitoring gap: surfacing Oracle ASM health and performance issues before storage pressure, rebalancing problems, or disk group failures become database incidents.

NHS and healthcare data on UK Sovereign Cloud: A compliance primer

Healthcare data sits at the top of the sensitivity hierarchy. Patient records are personal data under UK GDPR. Medical records are separately regulated under sector-specific frameworks. Clinical research data may be subject to research-specific rules. Genomics data carries residency implications that go beyond standard personal data protections. NHS data specifically is governed by frameworks that add UK public sector expectations on top of the healthcare-specific ones.

Synthetic Monitoring Is Broken. Your Production Traffic Can Fix It.

Synthetic monitoring has been a critical part of application reliability for years. It gives engineering and operations teams a way to proactively test applications, APIs, and critical customer journeys before users encounter problems. But there is a fundamental limitation with the traditional approach: Someone has to create the tests. As applications become more distributed and customer journeys become more complex, organizations can end up maintaining hundreds or even thousands of synthetic scripts.

Garbage in, garbage out: Splunk's Steve Flanders on why AI can't fix your bad telemetry

Cortex co-founder and CTO Ganesh Datta sits down with Steve Flanders, who leads AI transformation at Splunk and wrote the book on OpenTelemetry, to talk about why AI acceleration without strong observability foundations creates more problems than it solves.

AI budgeting: how to plan and forecast AI spend

AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.

Shipped: Monthly cost comparison in Explorer gets a glow up

Months have different numbers of days, and a monthly cost chart built on raw totals mixes that calendar difference into the trend. A 28-day February next to a 31-day March shows a 10.7% increase even when daily spend never moved. The same math works in reverse: real growth in a short month can look flat, hiding an increase worth investigating. That costs you time in two places. The first is triage.