I have a very large text-based dataset—about 10,000 × 10,000 entries—that needs exhaustive permutation matching. The goal is to scan every possible combination quickly in Python, identify all matches (or near-matches if you choose to add fuzzy logic), and return the results in a single Excel file ready for immediate analysis. Raw data and a short spec on what constitutes a “match” will be supplied; your task is to design an efficient, memory-savvy routine (pandas, NumPy, itertools, or any other high-performance approach you prefer) that can churn through roughly one hundred million comparisons without freezing up my workstation. Multithreading, vectorisation, or chunked processing—all are acceptable so long as they keep runtime practical and the output accurate. Deliverables • A well-commented Python script or notebook that performs the permutation matching on text data • A completed Excel file containing every identified match in a clear, tabular layout • A short read-me explaining any required libraries and how to rerun the job on new data I will test the script on my side with a control subset before running the full 10,000 × 10,000 sweep, so please make sure your code can accept variable input sizes.