Whether you’re managing code commits or tracking project tasks, integrating Jira with Azure DevOps can provide an informative, unified view of your development and deployment activities. In this step-by-step guide, we’ll walk through the process of connecting Azure DevOps to Jira using Git Integration for Jira – a versatile extension that enhances project management with real-time visibility and synchronization across your development pipelines.

As we move into the fall season here at StatusGator HQ, we wanted to update you on our progress the last 2 months. In between vacations, our team has been hard at work bringing you the next iteration of StatusGator. We have a ton of new stuff you’ve probably already seen but the highlights are announced formally below. Stay tuned for a few more LONG requested features over the next few months!

The telecommunications industry is no stranger to rapid technological advancements, but the integration of machine learning (ML) and generative AI is taking it to new heights. AI and ML are not just about technological transformation; they’re also revolutionizing people, processes, and the entire telco landscape. For tech enthusiasts and business leaders, understanding how these AI-driven innovations are shaping the future is crucial.

While building our Service Level Objectives (SLO) product, our team at Datadog often needs to consider how error budget and burn rate work in practice. Although error budgets and burn rates are discussed in foundational sources such as Google’s Site Reliability Workbook, for many these terms remain ambiguous. Is an error budget a static quantity or a varying percentage? Does burn rate indicate how fast I’m spending a fixed quantity, or is it just another way to express error rate?

Once upon a time, we thought “Log everything” was the way to go to ensure we have all the data we needed to identify, troubleshoot, and debug issues. But we soon had new problems: cost, noisiness, and time spent sifting through all that log data. Enter log analysis tools to help refine volumes of log data and differentiate signal from the noise to reduce mental toil to process. Log beast tamed, for now….

In the world of DevOps, where development and operations blend seamlessly, incidents are bound to happen. But the way these incidents are managed can make all the difference. Imagine a high-stakes race where every second counts—this is what DevOps Incident Management feels like. It's not just about putting out fires; it's about learning from each one to prevent future flare-ups.

Managing incidents efficiently is crucial to maintaining service quality. But handling every issue manually can be time-consuming, prone to errors, and overwhelming for your team. That's where Incident Management automation comes into play, revolutionizing the way IT teams respond to and resolve issues. Automation within Incident Management takes the guesswork out of the process, enabling faster response times and improving overall service delivery.

As part of our big tent philosophy here at Grafana Labs, we believe you should be able to access and derive meaningful insights from your data, regardless of where that data lives. One of the ways we stay true to that philosophy is through our Grafana Enterprise data sources.

Modern enterprises are more dependent than ever on data. That's why it's more important than ever for organizations to ensure that their data is accurate, reliable, and easily accessible. Data observability is a modern method that helps achieve this. It involves real-time monitoring of data to detect unusual patterns. By doing so, it ensures data quality and reliability, which boosts operational efficiency and governance.