Logs, Metrics, and Traces

Concepts covered: paObservabilitySignals

Three classes of signal show up in every observability discussion: logs, metrics, and traces. The vocabulary matters because each one answers a different question and has different storage and cost characteristics. Mixing them up produces dashboards that cost too much, alerts that fire on the wrong condition, and debugging sessions that bog down because the right signal is missing. The three are sometimes called the three pillars of observability. The framing comes out of the SRE community at Google and the distributed-systems community more broadly; it predates the data engineering specialization but applies cleanly to it. A pipeline is a distributed system whether the operator thinks of it that way or not, and the same observability vocabulary that serves Kubernetes clusters serves DAGs.

About This Interactive Section

This section is part of the Pipeline Operations: Beginner lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.