Compression and error handling

Modern column-oriented databases use compression to dramatically reduce storage. The compression ratio depends on the data type and the distribution of values. Understanding these techniques helps you design schemas that compress well. Compression Types Robust Error Handling Type conversion failures in production can crash entire pipelines. Robust error handling is essential for data engineering. These patterns handle progressively more complex scenarios. How should you handle type conversion on untrusted data? Applying Error Handling Patterns

About This Interactive Section

This section is part of the Data Types: 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.