Decorators

A decorator is a function that wraps another function, adding behavior without modifying the original. Decorators combine everything you've learned: higher-order functions, closures, and function objects. Decorators are everywhere in professional Python. Web frameworks like Flask and Django use them for routing. Testing frameworks use them for setup. Data libraries use them for caching. Understanding decorators is essential. The Decorator Pattern A decorator takes a function, creates a wrapper that adds behavior, and returns the wrapper. Let's build one step by step: The @ Syntax Practical Decorator: Timing One of the most useful decorators measures how long a function takes to execute: This pattern is extremely useful for performance profiling. Add the decorator to any function you want t

About This Interactive Section

This section is part of the Functional Programming: 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.