CI/CD for Data

Concepts covered: paCiCd

What They Want to Hear 'Every PR triggers: linting, unit tests, schema validation (fast, seconds). On merge to main: integration tests in staging (minutes). Before release: data diff on a staging subset (optional, hours). On deploy: canary rollout if the change affects critical pipelines.' The key insight: fast checks on every push, slow checks before release. Never skip the fast checks.

About This Interactive Section

This section is part of the Making It Repeatable: 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.