Operations | Monitoring | ITSM | DevOps | Cloud

The Windows Event log is an essential tool for administrators to investigate and diagnose potential system issues, but it can also be a daunting task to gain real value and separate useful log entries from noisy non-essential activity. Depending on the level of logging that can be useful, Windows events can span system issues, application-specific issues, and also dive into security type issues around unauthorized access, login failures, and unusual behavior.

At Sentry, we’re always looking for innovative ways to dogfood our product. Over the last year we added Sentry’s error monitoring to our developer environment so that we could better understand the health of it. In this blog post I’m going to touch on how fragile local development environments can be, how we brought observability into what’s happening by introducing Sentry, and what outcomes it has driven for our engineering organization.

In this article, we’ll cover the three main challenges you may face when maintaining your own Prometheus LTS solution. In the beginning, Prometheus claimed that it wasn’t a long-term metrics storage, the expected outcome was that somebody would eventually create that long-term storage (LTS) for Prometheus metrics. Currently, there are several open-source projects to provide long-term storage (Prometheus LTS). These community projects are ahead of the rest: Cortex, Thanos, and M3.

In the domain of cyber threat response, there’s a critical resource that every organization is desperately seeking to maximize: time. It’s not like today’s DevOps teams aren’t already ruthlessly focused on optimizing their work to unlock the greater potential of their human talent. Ensuring your organization to identify and address production issues faster – and increase focus on innovation – is the primary reason why Logz.io and its observability platform exist.

With the recent release of Loki 2.4 and Grafana Enterprise Logs 1.2, we’re excited to introduce a new deployment architecture. Previously, if you wanted to scale a Loki installation, your options were: 1) run multiple instances of a single binary (not recommended!), or 2) run Loki as microservices. The first option was easy, but it led to brittle environments where a heavy query load could take down data ingestion and problems were often difficult to debug.