Follow-up: Divide and Conquer in Data Engineering: A Data Cleaning Challenge in Leetcode

This blog post builds upon my previous article, "Divide and Conquer in ETL Pipelines and Big Data: A Data Engineer's Guide," which delves deeper into the broader applications of D&C in data engineering.

LeetCode question 2047, labeled as "Easy," But don’t judge a leetcode question based on its label because it might surprise you with its 29% acceptance rate, it certainly surprised me when I was working through this exercise. This seemingly simple question demonstrates the importance of data cleaning and highlights the challenges of working with unstructured text data.

Why is this seemingly easy question tricky?

Several factors contribute to the lower acceptance rate:

• Data Cleaning Complexity: The task involves cleaning text data, ensuring words adhere to specific rules regarding letters, hyphens, and punctuation. This requires careful attention to detail and consideration of various edge cases.

• Unfamiliar Concepts: The question might introduce unfamiliar concepts like allowlist vs. denylist checks, divided-and-conquer strategies, and extracting logic into separate functions.

• Lack of Practice: LeetCode users often focus on algorithmic problems, potentially neglecting data cleaning exercises like this one.

Why is this a valuable exercise for data engineers?

Data engineers frequently encounter messy and inconsistent data. LeetCode 2047 provides a practical scenario where you need to:

• Define Data Quality Standards: Establish clear rules for valid words, creating an allowlist of acceptable characters and patterns.

• Implement Data Cleaning Logic: Develop code to identify and remove invalid words based on the defined rules.

• Optimize for Efficiency: Employ techniques like early termination and divided-and-conquer to improve the performance of your data cleaning process.

Approaching the Problem:

1. Allowlist vs. Denylist:

   • Start by defining an allowlist of acceptable characters (lowercase letters, hyphens, and specific punctuation marks).

   • Consider the pros and cons of using a denylist instead, where you explicitly list all invalid characters.

2. Divided-and-Conquer Strategy:

   • Break down the problem into smaller, more manageable tasks.

   • Check for hyphens and punctuation separately, ensuring they adhere to the defined rules.

   • Extract the logic for validating words into a separate function for better readability and maintainability.

3. Early Termination:

   • Implement early termination conditions to exit the validation process as soon as an invalid word is encountered.

   • This improves efficiency by avoiding unnecessary checks on already invalid words.

Taking Your Time and Avoiding Common Mistakes:

• Don't Rush: Remember, data cleaning can be intricate. Take your time to understand the problem and carefully consider different approaches before diving in.

• Beware of Nested Loops: When working with nested loops, ensure you're using break statements correctly. Using continue in the inner loop will only move to the next iteration of the inner loop, not the outer loop.

• Consider Alternative Approaches: While the provided solution is clean, other approaches like using a combination of a while loop and isValidChar are also possible. Experiment and choose the most readable and efficient solution for your specific needs.

Conclusion:

LeetCode 2047, despite its "Easy" label, offers a valuable learning experience for data engineers. It demonstrates the importance of data cleaning, allows you to practice essential skills, and aligns with real-world data engineering challenges.