Why Parquet?

Concepts covered: paColumnarVsRow

This is asked as a screener because it instantly reveals whether you've worked with production data at scale. The interviewer doesn't want "it's columnar." They want you to connect physical layout to the queries you actually run. Row vs. Columnar Layout CSV and JSON store data row-by-row. To answer "what's the average order amount?" on a 500-column table, a row-oriented reader must load all 500 columns into memory, skip 499 of them, and aggregate the one it needs. Parquet stores each column contiguously on disk. That same query reads exactly one column - roughly 0.2% of the total bytes for a wide table. Compression and Encoding Parquet compresses well because columns contain homogeneous data. A status column with 5 distinct values across 100M rows uses dictionary encoding - each value

About This Interactive Section

This section is part of the The Storage Question: 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.