File Format Depth

Concepts covered: paColumnarVsRow

The #1 Screening Question 'Why Parquet?' is asked in over half of pipeline interviews as a screening question. Here is the three-word answer: columnar, compressed, predicate-pushdown. Then expand: 'Parquet stores each column separately, so analytical queries that only need 3 out of 100 columns read 97% less data. Same-type values grouped together compress 10x better than mixed-type rows. And row group statistics let the engine skip entire chunks without reading them.'

About This Interactive Section

This section is part of the How Data Moves: Intermediate 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.