This post shows how to use Datadog Real User Monitoring (RUM), Datadog Workflow Automation, and Datadog Agent Builder to automatically generate a weekly, executive-ready web performance report—without dashboards, manual analysis, or slide decks.

Datadog Automatic APM instrumentation is generally sufficient for most customers, but as soon as it’s not there can be a pretty significant engineering burden. An engineer will have to go into the codebase, find where the changes need to be made, refer to documentation to ensure they are using the correct pattern, and so on.

Datadog dashboards don’t fail from missing metrics—they fail from rushed design and legacy thinking. Using the Datadog MCP Server + AI assistants, we validate metrics and context before writing JSON, then build dashboards faster. Real cloud cost example from our environment.

A clean recap of December’s NoBS Datadog Office Hour, covering real Datadog production issues, troubleshooting questions, and the answers shared live by our engineers.

A practical, engineer-first guide to migrating to Datadog — including tagging, Agents, dashboards, logs, APM, synthetics, SLOs, validation, and cost control.

APM and USM may look similar, but they solve different problems. Here’s how to choose the right one — and what to do when your stack doesn’t support either.

Over the past few years, Datadog has introduced many strategies for log storage, but it’s pretty rare that we see many of these used effectively.

A wise man named Ice Cube once said “chickity-check yourself, before you wreck yourself.” If you’re not a fan of wrecking yourself, we’ve got something for you—the Reality Check, a brutally honest report of what’s going on in your Datadog and how to fix it…

Datadog Synthetic Monitoring is a powerful way to test uptime, API performance, and user flows — but without best practices, costs and SLOs can spiral fast. Here are practical tips on how to set up Datadog Synthetic tests the right way.

We got a flood of real-world questions during our App Builder webinar—way more than we could tackle live. This post dives into the details, clears up confusion, and gives straight answers to the things you actually wanted to know.

I-shaped. T-shaped. Π-shaped. Tree-shaped. Cute on paper. Useless when your stack is on fire. Observability doesn’t need specialists or generalists. It needs engineers who can tie systems together, learn fast, and make sense of chaos. At NoBS, we call them Arcane Engineers—and yes, Yog-Sothoth is involved.
This blog explains why.

Everyone loves to guess what SREs do—but most of them get it wrong. It’s not just about “keeping things up.” It’s about smarter observability, killing off useless toil, and reducing the chaos that wakes teams up at 3am. We wrote this piece to clear up the myths and share what modern SRE work actually looks like—messy, valuable, and mission-critical.

DASH 2025 certainly did not disappoint. Datadog dropped a pile of announcements right out of the gate. We figured we’d break out a few of our personal favorites for you.

I’ve spent a good amount of time recently working in the App Builder, understanding its capabilities, and how some of it works. While there are a lot of examples in the form of blueprints that are provided by Datadog, figuring out how some more of the functionality really works has been tricky, since the documentation is still a work in progress. So, I did some of the hard parts, so you don’t have to!

Seems like there's a conference for everything, to the point where if you tried to attend all of them you would never get any work done. As an engineer, our time is valuable, and if we’re not gaining value in doing something then we shouldn’t be doing it.

Messy tagging slows down troubleshooting, clutters dashboards, and makes alerts unreliable. In Part 2, we’re tackling the biggest mistakes—over-tagging, inconsistent naming, and skipping Unified Service Tagging—plus how to fix them.

We received a lot of great questions in our webinar. Hopefully, this post helps answer those questions and provide even more detail than we could cover.

Woah now, hold on a second - get your mouse away from that “New Dashboard” button, we’ve got a few things to talk about first. Clearly you’re well on your way to using Datadog.

TL;DR: If your Datadog tags are messy, your observability is messy. Stop overcomplicating it—use layers, keep it practical, and only tag what matters.

Service Level Objectives (SLOs) sound simple—define what good service looks like, measure it, and call it a day. But getting them right is tricky.

Before we take the idea of observability and what it is off the rails, we should probably start with the boring definition of it just so we’re all on the same page.

Thanks for checking us out. We’re here because we’re done with BS in the tech and sales industries. We’re here to do things differently.