Operations | Monitoring | ITSM | DevOps | Cloud

Google SecOps (formerly Chronicle) is widely used for large-scale security analytics, long-term telemetry retention, and detection across diverse environments. Its Unified Data Model (UDM) enables correlation across sources and supports analytics that operate over long time horizons. To take full advantage of these capabilities, security data must arrive in a consistent and well-structured UDM format. In practice, this is rarely the case.

Stop writing fragile scripts. Discover how top enterprises use Kubernetes Management Platforms to automate governance (Policy-as-Code), scale ephemeral environments, and enforce FinOps with Spot Instances.

Enterprises operating at cloud scale today face a growing reality: managing infrastructure performance and cost in silos no longer works. Kubernetes, multi cloud environments, and GPU accelerated workloads deliver immense agility and capability, but they also introduce complexity that outpaces traditional monitoring and cost governance approaches.

When you move past daily rotations but find anything longer than a week feels too stretched out, you often end up choosing between weekly and split-week rotations. Weekly rotations give you a full seven days before handing off. Split-week rotations break that time into smaller chunks like 2-day, 3-day, or 4-day shifts. Each approach creates a different rhythm for your team. This guide compares both patterns across three key criteria.

Q4 2025 delivered major upgrades across Harness Continuous Delivery, GitOps, and Continuous Verification, focused on safer rollouts, stronger infrastructure integrations, and workflows that scale. Here’s a curated roundup of what shipped and where to learn more. Welcome back to the quarterly update series! Catch up on the latest Harness Continuous Delivery innovations and enhancements with this quarter's Q4 2025 release. For full context, check out our previous updates.

For most of the last decade, the direction of cloud strategy was clear: standardize, consolidate, and reduce sprawl. Engineering teams worked to pick a primary cloud, reduce vendor dependencies, and simplify their stacks. FinOps teams unwound years of fragmentation. Platform teams built guardrails to make sure it didn’t happen again. Then AI arrived, and it’s a fundamentally different class of workload. AI demands specialized hardware and, increasingly, diverging providers.