Operations | Monitoring | ITSM | DevOps | Cloud

OpenSearch is a powerful, scalable search and analytics engine that can do amazing things for logging, observability, and full-text search. But like any distributed system, it only performs well if you keep it properly tuned and healthy. Ignore it, and you risk slower queries, higher costs, and even data loss. Here are five practical tips to keep your OpenSearch cluster running smoothly and efficiently.

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.

As businesses scale, so do their observability needs, but many find themselves stuck with costly, inflexible platforms that no longer serve them. Despite mounting frustrations, the complexity of migration keeps companies from making a change. The risk of losing critical data, disrupting workflows, or rebuilding everything from scratch often outweighs the benefits of switching. Most vendors offer little to no migration support, forcing teams to manually reconfigure dashboards, alerts, and integrations.

When you hear “spill the tea,” you probably think of pop culture, not outages or anomalies. But the origin may surprise you: before it was slang for juicy gossip, ‘tea’ was actually ‘T,’ which represents truth. We know what you’re thinking: “Are you trying to say ‘spilling the tea’ is a good thing?” And yes, that’s exactly what we’re saying, especially when your logs are doing the talking.

OpenTelemetry is emerging as the common framework for collecting observability data, and for good reason. It’s vendor-neutral, open source, and designed to collect traces, metrics, and logs in a consistent way. But while most of the buzz is around tracing and metrics, let’s not forget: logs are still the backbone of investigation and response. That’s why Graylog now supports native collection of OpenTelemetry data over gRPC.

Cyber threats are growing more cunning every day, with attackers even tapping into artificial intelligence to outsmart traditional defenses. Organizations face a flood of security data—logs, alerts, and telemetry—making it nearly impossible to sift through. How do you spot the real dangers amid all that noise? Observo AI’s ML-Powered Threat Insights offers a game-changing answer.

In an era where hybrid environments and AI-driven innovations redefine enterprise operations, organizations face increasing complexity, disruption, and vulnerability in their systems. To overcome this growing challenge, Cisco and Splunk are working together to harness the power of AI to help customers ensure that digital resilience is an inherent part of their systems.

As security and DevOps teams grapple with rising telemetry volumes and ballooning tool costs, they’re increasingly forced into tradeoffs: index less, ingest less, search less. That might save money in the short term—but it comes at the cost of impairing long-term visibility, historical investigation, and strategic clarity. Traditional search tools are built around precision. They’re useful when you know what you’re looking for—but far less helpful when you don’t.