Operations | Monitoring | ITSM | DevOps | Cloud

The complexity and scale of modern infrastructure requires an equally intelligent set of observability tools to effectively monitor it. Remember when scaling meant ordering new servers and racking them in a data center? Remember when cloud providers first offered access to seemingly infinite virtual machines at the click of a button? Remember when Kubernetes made it trivial for infrastructure to automatically scale itself based on demand?

In July’s episode of This Month in Datadog, we’re doing things differently by spotlighting the people behind the products you rely on. Jeremy is joined by Tristan Ratchford to discuss saving time and effort when you’re on call with Bits AI SRE, and by Kevin Hu to explore gaining visibility into datasets across the entire data lifecycle with Data Observability.

In Kubernetes environments, applications often communicate with the Datadog Agent to send telemetry data such as custom metrics via DogStatsD or traces through Datadog APM. How this communication takes place depends on the communication mode set on the Datadog Cluster Agent's Admission Controller. With the sockets option, communication takes place through local inter-process communication via Unix domain sockets (UDS), whereas the service and default hostip options rely on network communication.

CI/CD pipelines are automated workflows that take code from development to production. The CI/CD pipeline meaning encompasses two key practices: A typical CI/CD pipeline includes stages like code compilation, testing, security scanning, artifact creation, and deployment across multiple environments.

Almost every other day, when I scroll past r/devops or r/sre, I see a post like this asking how a dev can get started with devops, observability, etc. Sample Reddit thread on how to get started with OTel This blog is an attempt for anyone lost to find their way into observability and a wake-up call for devs to they should think about observability more actively today than ever before. A dev’s observability playbook.

Every day I seem to run into yet another post with someone solemnly opining that “writing code has never been the hardest part of software engineering. And hey, that’s smashing. As an engineer from the ops/infra/SRE side of the house, I feel like I’ve been saying this my whole career. (Is there anything more satisfying than being proven right in public? Not in my book.) So, which is it?

As enterprise IT environments evolve into ever-greater complexity and scale, demands on operations teams are accelerating. In the traditional model, observability tools collect data, engineers manually correlate events, and remediation follows a ticketing trail. However, that approach no longer matches the speed and scale of today’s digital businesses. Even the most storied dashboards can’t address today’s operational needs.

You don’t need to be a power user to get powerful results. I’m not training models or prompting GPTs into poetry—I’m just using them to do what great managers already try to do: communicate clearly, prioritize outcomes, and lead with intention. Over the last few quarters, I’ve built a handful of custom GPTs to support my weekly, monthly, and quarterly workflows.

Developers have long used profilers to diagnose performance bottlenecks and improve the efficiency of their code. But a modern version of profiling, continuous profiling, is quietly redefining what profiling is and what it can do. By running nonstop in production with very low overhead, continuous profilers give teams always-on visibility into how their code behaves in the real world.

Great data is a prerequisite to all things AIOps and observability. Great observability data results in fewer observability gaps, better analysis and insights, and more confidence within teams that rely on the power of modern AIOps and observability technologies. Goals for improved automation, IT efficiencies, intelligent triage and remediation all become more achievable with better data.