Operations | Monitoring | ITSM | DevOps | Cloud

Distributed traces track how your system processed a single request — not what your customers did over time. Confusing the two leads to poorly instrumented systems.

Find and fix slow SQL queries using execution plans, missing index detection, N+1 pattern fixes, and pagination strategies for PostgreSQL and MySQL. Product Marketing Manager.

How database indexes work, when to use B-tree vs hash indexes, clustered vs non-clustered indexes, and how to tell if your indexes are actually helping.

January 2026 Release Here's a dirty secret about observability: most teams find out about outages from their customers. Not from their dashboards. Not from their alerts. From angry tweets and support tickets. The excuse is always the same: "We have metrics! We have dashboards! We even have that AI thing now!" And yet, somehow, your checkout endpoint has been returning 502s for forty-five minutes and you're learning about it from the VP of Sales who just got off a call with your biggest customer.

TL;DR: Prometheus pays cardinality costs at write time (memory, index). ClickHouse pays at query time (aggregation memory). Neither is "better":they fail differently. Design your pipeline knowing which failure mode you're accepting. -- Every month, someone posts "just use ClickHouse for metrics" or "Prometheus can't handle scale." Both statements contain a kernel of truth wrapped in dangerous oversimplification.

When it comes to containerization technologies, Podman and Docker are the two giants that often come up in conversation. Both have revolutionized how we build, deploy, and manage containers, but what sets them apart? In this blog, we'll dive deep into a side-by-side comparison of Podman and Docker. We'll cover everything from architecture to security, performance, and compatibility.

When it comes to monitoring and observability tools, Datadog is often one of the first names that comes to mind. But while Datadog’s features are widely discussed, its pricing often remains a topic of confusion. How much does Datadog cost, and what factors influence your bill? This guide breaks down Datadog pricing to help you better understand its structure, hidden nuances, and whether it’s the right fit for your needs.

High-cardinality metrics are one of those ideas that sound obviously right - until you try to use them in production. In theory, they promise precision. Instead of averages and rollups, you get specificity: per-request, per-userid, per-container, per-feature insights. The kind of detail we all immediately want when something is on fire. And then things start breaking. Not immediately. Not loudly.But quietly.