Operations | Monitoring | ITSM | DevOps | Cloud

VictoriaMetrics is a fast, cost-efficient, and highly scalable time-series database designed as a drop-in replacement for Prometheus storage. It is widely used for collecting, storing, and querying metrics at scale, while remaining lightweight enough to run as a single binary or container. Because it is fully Prometheus-compatible, VictoriaMetrics supports standard PromQL queries and integrates seamlessly with Grafana.

In today's global enterprises, distributed infrastructure is the norm, not the exception. Organizations operate across continents and are driven by customer proximity and regulatory requirements. For the Elastic Stack, this reality often translates into a multi-cluster deployment model, where data is collected and stored in multiple geographically dispersed Elasticsearch clusters. But, why adopt complexity? The decision to decentralize data storage is generally driven by three critical factors.

This article is part of our series on how Datadog’s engineering teams use LLM Observability to iterate, evaluate, and ship AI-powered agents. In this first story, the Graphing AI team shares how they instrumented their widget- and dashboard-generation agents with LLM Observability to detect regressions and debug failures faster. Visibility into how large language model (LLM) applications behave in real time is essential for building reliable AI-driven systems at Datadog.

Modern, hybrid environments change continuously. But, legacy ITOps workflows assume stable infrastructure. IT environments don’t behave in predictable ways. Infrastructure changes continuously, services spin up and shut down on demand, and data formats evolve with every deployment. Most ITOps workflows, however, are still designed around the assumption of stability. That mismatch drives failure. Static runbooks expect environments to stay put.

If you're working in IT monitoring and observability, you simply cannot ignore the power of Datadog and New Relic. These two tools have plenty of features that can revolutionize your entire observability strategy and give you complete control over your infrastructure. These tools are built so as to capture the tiniest of details, be it on applications, infrastructure, databases, servers, or something completely on the cloud.

The best tool to track, analyze, and manage errors at scale? Rollbar tops our list.

Even though the shift to the cloud has slowed recently as many businesses are moving certain workloads back on-premise, Microsoft Exchange remains one cloud-based service that most organizations continue to embrace – despite its frequent outages. This doesn’t come as a surprise, as Microsoft has successfully devolved on-prem Exchange Server – the only viable alternative – into an unfriendly dragon that even experienced sysadmins won’t touch with a 10 ft pole.

Observability pricing often seems reasonable at the outset, but many organizations discover their real complexity only as environments scale and usage patterns change. As environments grow more complex and hybrid by default, many organizations struggle with rising costs, fragmented tools, and pricing models that complicate cost predictability and long-term planning.

Security data collection has become a first-class architectural concern for modern SOCs. Once collection is treated as a dedicated layer, separate from analytics and detection, the next question becomes practical: how should telemetry be collected in a way that aligns with this architecture? In the previous article, we examined why this shift occurred. Here, we focus on how different collection models (agent-based, agentless, and hybrid) fit into modern security data collection architectures.

Let’s start with a question. What is DevOps all about? I’ll tell you my answer. In retrospect, I think the entire DevOps movement was a mighty, twenty year battle to achieve one thing: a single feedback loop connecting devs with prod. On those grounds, it failed. Not because software engineers weren’t good at their jobs, or didn’t care enough. It failed because the technology wasn’t good enough.