Operations | Monitoring | ITSM | DevOps | Cloud

Following the popularity of our existing GitHub integration, we’ve extended similar capabilities to GitLab users. You can now ingest GitLab events directly into Mezmo Telemetry Pipelines and route them to any destination. This provides a powerful new way to monitor, alert, and react to activity within your GitLab repositories.

Let's be honest: most conversations about AI ethics sound like they're happening in a boardroom, not an ops room. But here's the thing, when you're using AI to make sense of your telemetry data, ethics isn't some abstract concept. It's the difference between insights you can trust and algorithmic noise that leads you down the wrong path. The uncomfortable reality? Your AI is only as ethical as the messiest, most biased piece of telemetry data you feed it. And if you think your data is clean, well...

‍ We're not witnessing the end of observability, we're witnessing its evolution into something far more powerful. The observability industry is having its Moneyball moment. Just like Billy Beane revolutionized baseball by using data analytics to compete with teams that had vastly larger budgets, observability is undergoing a fundamental transformation.

Most people are probably familiar with the word “grok” from Robert A. Heinlein’s novel A Stranger in a Strange Land, in which it is used to describe a deep, almost mystical understanding of something. ‍ Grok is also the name of a plugin for LogStash that enables you to parse and analyze log data using a syntax similar to regular expressions, but specialized for various log formats and fields.

You’ve probably felt the pain: data pouring in from every corner of your stack, tools choking on volume, dashboards lagging behind reality, alerts firing (or worse, not firing) without context. If that sounds familiar, it’s time to get serious about telemetry pipelines. Whether you're an SRE trying to stabilize a flapping service or a developer navigating multi-cloud chaos, a telemetry pipeline helps you take control of the data firehose.

It’s 3 AM. Your phone screams. You stumble to your laptop, eyes half-closed, wondering the same question every SRE has asked mid-incident.

Log volume is exploding, costs are rising, and most teams are stuck duct-taping together short-term fixes. During our webinar, "Optimizing Log Management in Datadog: Cut Costs Without Losing Insights," we discuss how DevOps and engineering leaders are navigating the growing pains of observability, especially in environments where tools like Datadog are mission-critical but challenging to manage. Here’s a recap of the key takeaways.

Datadog may be the default choice for all-in-one observability, but its logging experience takes a back seat to the broader platform. Logs are primarily designed to feed into metrics and traces, which leads to tradeoffs such as slower search, complex workflows, and a UI that isn’t optimized for log investigations. As a result, Datadog doesn’t align with how developers actually troubleshoot.

By Sara Miteva Sr. Product Marketing Manager, Checkly ‍ Modern applications are complex. Microservices, third-party dependencies, and continuous deployments all contribute to a flood of telemetry data—logs, metrics, traces—flying in from every direction.

At Mezmo, we’ve always believed that observability should empower innovation, not hold it back with complexity and unpredictable costs. However, as organizations scale and data volumes continue to explode, the old ways of managing telemetry data aren’t sustainable.