Operations | Monitoring | ITSM | DevOps | Cloud

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….

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.

Integrating MQ monitoring into a newly modernized mainframe environment isn’t something you can just wing. We’ve worked on projects where it seemed straightforward at first—just plug in some monitoring tools and you’re good to go, right? Not quite. The reality is, if you don’t approach this with a plan, you’ll find yourself tangled in a web of configuration headaches and performance hiccups.

AWS MediaTailor provides a wealth of information via metrics, but one key feature that is very difficult to track is the Transcoding performance. What is transcoding and why is performance important?

This month I continued adding features to OnlineOrNot's status page functionality, while also taking the time to fix bugs, and add an integration for Pingdom.

As the Managed Service Provider (MSP) landscape continues to evolve, developing a robust MSP monitoring strategy is essential for MSPs wanting to stay ahead in an increasingly complex digital environment.Rapid advancements in technology, coupled with the growing complexity of IT environments, necessitates a shift in how MSPs approach monitoring. The fiercely competitive MSP market and external pressures on costs such as cloud pricing mean that customer expectations are high but profit margins slim.