Operations | Monitoring | ITSM | DevOps | Cloud

When an application slows down, users rarely think about the database—but in many cases, that’s where the bottleneck lies. Databases sit at the core of nearly every application, storing, retrieving, and processing the information that powers business transactions, analytics, and user interactions. A minor inefficiency in query execution or a spike in resource usage can cascade into multiple issues, starting with degraded application performance, service interruptions, or even downtime.

Today’s enterprise networks aren’t constrained to a single location—they span continents, clouds, and providers, and they’re relied upon by users who can work from anywhere. For network operations teams, that means every issue is a potential scavenger hunt. Is it the app? The WAN? The cloud provider? The ISP? The stakes are high and your tools need to evolve. That’s why the integration of DX NetOps and AppNeta is such a game-changer.

Running a data center efficiently is no small feat. From managing energy costs to preventing downtime, there's a lot that can go wrong—and a lot that can be optimized. Discover 10 actionable ways to enhance your data center operations, with practical tips on how Hyperview DCIM software can help you achieve these improvements more easily and effectively.

The risk of headline-grabbing incidents, like Samsung’s ChatGPT data leak, related to AI usage outside of the authorization and control of IT (a.k.a. shadow AI) is clear. Most IT teams recognize that a high-profile incident can have serious repercussions. However, the risk of shadow AI goes well beyond the risk of a single incident. In fact, the recent Komprise IT Survey indicates that 79% of organizations have experienced negative outcomes from sending corporate data to AI.

MongoDB’s strength of flexible schema and fast iteration can also hide costly queries until they surface as user-facing latency, replica lag, or spiky CPU. A handful of slow operations can impact the cache, starve other workloads, and cascade into timeouts across services. Monitoring slow queries gives you an early warning system for index gaps and query-plan regressions introduced by code deploys, schema changes, or shifting data shapes.

There’s a new way to ingest AWS metrics into Grafana Cloud that makes observing your AWS resources more cost-effective, easier to operate, and more accurate. You can now stream metrics into the AWS Observability app in Grafana Cloud in near real-time thanks to our new integration with Amazon CloudWatch and Amazon Data Firehose. We’re already using it internally, and we’re finding that it’s not only easier to operate—it’s at least five times more cost-effective.

In the August episode of This Month in Datadog, Jeremy shares how you can make more informed cloud cost decisions, gain insights into your LiteLLM-powered applications, and secure Kubernetes infrastructure with Datadog Workload Protection. Later in the episode, Danny puts the spotlight on Datadog Kubernetes Autoscaling, which helps you deliver cost savings without sacrificing performance.

There are a lot of ways to gauge the performance of your DevOps teams and the health of your software, but DORA metrics have emerged as the industry standard. If you aren’t familiar with DORA metrics, take a few minutes to read this comprehensive guide to understanding DORA metrics. DORA metrics were designed to offer a high-level, long-term view of how your teams are performing.

When we started building Logs in Sentry we had one goal: make them useful for real debugging, not just another high-volume text storage. This meant making them "trace connected" from day one. This let us ensure they were tightly connected to the actions and performance happening in your application, right where developers already go to investigate errors, performance, and latency issues. Now, Logs is out of beta and generally available to everyone.

APM tracing records the complete execution path of a request as it travels through your system, including database queries, external API calls, cache lookups, message queue events, and inter-service requests. Each step is captured with precise start and end timestamps, duration, and context such as service name, operation name, and relevant attributes. This lets you pinpoint where latency or errors originate without piecing together metrics and logs manually.