In the Part 1, we saw that a scalable pipeline architecture consists of data collection, processing, storage, and querying stages, with key design principles including horizontal scaling, stateless processing, and backpressure management. Now, let's examine specialized scaling strategies for each telemetry signal type. Scaling Observability: Designing a High-Volume
Thought Leadership
Building observability in large-scale, cloud-native systems requires collecting telemetry data (metrics, traces, and logs) at extremely high volumes. Modern platforms like Kubernetes can generate millions of metrics, traces, and log events per second, and enterprises often must handle this flood of telemetry across hybrid environments (on-premises and cloud). Designing a
How To
Goal: Spin up Prometheus+Alertmanager, Loki+Promtail, Jaeger, and Grafana with a single docker‑compose.yml, then watch a tiny Java HTTP service emit metrics, logs, and traces—all in less than an hour. Why this post? You keep hearing that “observability ≠ monitoring” and that you need metrics, logs, and
Tooling Deep-Dives
OpenTelemetry’s Collector is a vendor-neutral service that sits between your applications and observability backends. It can receive telemetry data (traces, metrics, logs), process or transform it, and export it to one or multiple destinations. In a production environment, the Collector becomes essential for building a flexible and resilient observability
OpenTelemetry Corner
OpenTelemetry (OTel) has quickly become a cornerstone of modern observability. If you’re a developer or engineer looking to instrument your applications for better insight, this beginner’s guide is for you. I’ll explain what OpenTelemetry is, why it matters, and walk through a step-by-step tutorial to instrument a
Fundamentals
Welcome to the world of observability! If you’re new to this field, all the jargon and acronyms can feel overwhelming. But fear not—this beginner’s cheat sheet will walk you through the essential observability terms in plain language. Use it as a reference whenever you encounter an unfamiliar
Getting Started
Imagine you’re a detective for software systems. Late one night, an alert goes off: something is wrong with your application. But what is wrong? In a complex microservices environment, finding the culprit can feel like searching for a needle in a haystack. This is where observability comes in. Observability
