Quality SLAs vs Ops SLAs

Concepts covered: paQualitySla, paOperationalSla

An SLA states a commitment. The pipeline-as-product framing from Lesson 1 introduced two SLAs as elements of the contract: freshness SLA and quality SLA. They are commonly conflated. They are different commitments to different things, with different consequences when they fail. A pipeline that meets its operational SLA can fail its quality SLA in green. A pipeline that meets its quality SLA can miss its operational SLA without affecting correctness. The producer who treats both as one number ends up over-promising on one and under-detecting failure of the other. The conflation has visible consequences. Status pages that report a single uptime number describe operational SLA exclusively, leaving consumers with no way to distinguish 'late but correct' from 'on time but wrong'. Incident revie

About This Interactive Section

This section is part of the Data Quality and Contracts: 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.