Operations | Monitoring | ITSM | DevOps | Cloud

You know that uneasy pause before opening your monitoring dashboard? The one where you're hoping nothing's broken—but a part of you knows something probably is. Performance issues often start quietly: a few slow endpoints, a checkout that takes longer than usual, a graph that looks a little off. Before long, those small signals turn into alerts and support tickets.

Automating chaos engineering with Terraform eliminates manual setup across environments by enabling you to version control your entire chaos infrastructure, from service discovery to security governance policies. The Harness Terraform provider supports end-to-end automation including Kubernetes infrastructure setup, custom image registries, Git-based ChaosHub management, and granular security controls that ensure safe experiment execution in production.

Behind every reliable dashboard and morning report stands a system of accountability: SQL Server Agent jobs. They’re what keeps backups on time, analytics flowing, and data pipelines steady when everything else moves fast. However, to sustain that reliability, SQL Server Agent must operate with precision at scale.

This post is a deep dive into how we improved the P95 latency of an API endpoint from 5s to 0.3s using a niche little computer science trick called a bloom filter. We’ll cover why the endpoint was slow, the options we considered to make it fast and how we decided between them, and how it all works under the hood.

If you’re like most developers, you know that writing code isn’t the bottleneck anymore. AI has made it faster than ever, and chances are you’re already using it. Yet, delivering software is still complex because of everything else you have to manage: fixing vulnerabilities, reducing tech debt, cleaning up feature flags, ensuring test coverage, writing documentation, and the list goes on. That’s why we built Rovo Dev, a context-aware AI agent for developers.

Traditionally, companies had to use separate tools for ETL, data storage, and analytics. Often, this resulted in slow, complex, and expensive data workflows A good example is PwC’s Deals, Insights & Analytics (DIA) team, which faced similar challenges. According to Microsoft, bespoke solutions often took months to build and were difficult to merge, slowing projects and driving up costs. That changed when PwC adopted Azure Synapse Analytics. The result?