Postmortem: Cadence Change Bug

Concepts covered: paOrchestrationPostmortem, paAssetTriggerSeam, paFreshnessSla

A real production incident, redacted from a fintech postmortem. A daily DAG named `daily_finance_close` had been running cleanly for fourteen months. In month fifteen, it began missing its 6am Pacific SLA twice a week. Nothing in the DAG had changed. The investigation revealed that an upstream Stripe ingestion DAG had been quietly migrated from a 30-minute cadence to a 5-minute cadence three weeks earlier. The change was a clear improvement upstream. It broke the downstream because of every assumption the original architecture had encoded. The Original Architecture What Changed Upstream and Why It Started Failing The upstream team migrated `stripe_to_raw` from 30-minute full pulls to 5-minute incremental pulls. The migration was a clear improvement: smaller batches, lower latency, less loa

About This Interactive Section

This section is part of the Orchestration and Dependencies: Advanced 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.