Data Contracts and CI Enforcement

Concepts covered: paDataContracts, paContractCi

Lesson 1's advanced tier introduced the pipeline-as-product framing: a pipeline has a contract that names producer, consumer, schema, freshness SLA, quality SLA, backfill policy, and deprecation policy. This section turns that framing into a working mechanism. A data contract is the executable form of the commitment. The producer commits to a shape and a set of guarantees; the consumer relies on them; the contract is checked in CI on every change so violations cannot ship. Without enforcement, contracts are documentation. With enforcement, they are the layer that prevents most of the silent failures the previous lessons spent so much effort detecting. The shift from documentation to enforcement is the central move. Documentation rots. CI does not. A contract that is a YAML file in a wiki p

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.