The Small File Problem

Concepts covered: paPartitioning

What They Want to Hear 'Small file problem. Over-partitioning or high-frequency writes create thousands of tiny files. The per-file overhead of open/read/close dominates query time. I fix it with compaction: merge small files into 128 MB to 1 GB targets. Delta OPTIMIZE or Iceberg rewrite_data_files.' This is the #1 practical storage question. Most candidates know partitioning but cannot diagnose why their partitioned table is still slow.

About This Interactive Section

This section is part of the Where Data Lives: 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.

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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.