Caching Strategies

Caching is one of the most impactful performance techniques in data engineering. By storing the results of expensive computations or database queries, you avoid repeating work. Python provides built-in caching tools, and understanding how to build custom caches using data structures gives you fine-grained control over eviction policies, size limits, and expiration. Using functools.lru_cache Building a Custom LRU Cache The LRU eviction policy works well for workloads with temporal locality - recently accessed items tend to be accessed again soon. For frequency-based workloads, consider LFU (Least Frequently Used) strategies instead.

About This Interactive Section

This section is part of the Data Structures: 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.