Operations | Monitoring | ITSM | DevOps | Cloud

Velocity isn’t just about how fast developers write code—it’s about how efficiently that code moves from idea to production. Yet many teams struggle with an invisible bottleneck: inconsistent workflows. Without clear, standardized processes, teams hit avoidable roadblocks that slow them down in ways that aren’t always obvious. PRs linger in review, merge conflicts become routine, and onboarding a new developer feels like an unwritten scavenger hunt. The worst part?

Network operations teams are under constant pressure to ensure optimal performance and availability. But in today's complex network environments, gaining a clear picture of what's happening is difficult. Without a reliable method of collecting performance metrics across your most critical connections, identifying the root cause of slowdowns or outages becomes a frustrating and time-consuming process.

Since joining Stackify in 2014 during the early stages of the startup company, I have had the privilege of working in various roles along the way. I began in customer support, moved to the QA team, and am now the product manager for Stackify Retrace.

In the last few months I've had the privilege to implement and witness new and exciting Cycle platform workflows emerging. Some of these come from the evolution of our own internal work, while others I've learned from our users. Reflecting on this, I thought it would be beneficial to the community to share! In this article, I'll walk through a handful of the neatest patterns we've seen in action. So I hope you'll join me for a no fluff, real, and practical look at how to get more out of Cycle.

The IT landscape for delivering applications and other services to end users has shifted to a hybrid deployment model, and this change is here to stay. While it provides myriad benefits for IT teams and their organizations, it also complicates the cybersecurity landscape, which needs protecting. Attackers continuously find new techniques to bypass traditional security measures.

According to McKinsey research, 70% of digital transformation projects fail to meet the stated goals. Depending on the reasons for launching a digital transformation project, this failure can lead to loss of productivity, security, profitability, or any other number of costly outcomes. In today’s competitive landscape, businesses cannot afford this failure – and yet it continues. Why?

In 2020, we launched the Google Sheets data source for Grafana, providing organizations with real-time data visualization capabilities for all their go-to spreadsheets. Since then, thousands of users have installed the data source to quickly and easily derive insights from their spreadsheet data. In this blog post, we’ll explore key features of the Google Sheets data source, as well as some helpful resources to install and start using the data source today.

The AI (artificial intelligence) landscape wouldn’t be where it is today without AI-as-a-service (AIaaS) providers like OpenAI, AWS, and Google Cloud. These companies have made running AI models as easy as clicking a button. As a result, more applications have been able to use AI services for data analysis, content generation, media production, and much more.

There’s an old cliche about sales and jobs to be done - no one wants to buy a drill, they need a hole… actually, they want a home with pictures on the wall. To get to that beautifully designed home, they will buy a drill, make holes for brackets that can support their various artwork and family photos, and progress toward their dream home experience. Similarly, no one wants to buy observability software. They want their mean time to resolve (MTTR) issues to be zero.

For a tool like Netdata, monitoring crashes and abnormal events extends far beyond bug fixing—it’s essential for identifying edge cases, preventing regressions, and delivering the most dependable observability experience possible. With millions of daily downloads, each event provides a vital signal for maintaining the integrity of our systems.