Basic DE Applications

Concepts: pyTopKFrequent, pySlowestQueries, pyKClosest

Here is where you convert a coding answer into a data engineering answer. Heap and top-K problems are not just LeetCode exercises. They show up constantly in DE work: finding the most frequent log errors to prioritize, identifying the slowest queries for optimization, finding the K closest events to a target timestamp for alignment. Every time you frame your heap solution in terms of real DE problems, the interviewer writes 'strong domain understanding' on the scorecard. Top-K Most Frequent Log Errors The Counter + nlargest pattern is the most common real-world heap problem in DE. Log aggregation, error monitoring, and usage analytics all reduce to this: count frequencies, find the top K. In production, you might be doing this over a Spark DataFrame with groupBy().count().orderBy(desc()).l