You are tasked with optimizing a data processing Python application. The application reads large CSV files (500MB+), processes the data through several transformations, and outputs aggregated statistics to a database. The current implementation takes approximately 45 minutes to process a single file. Identify and implement at least 5 different optimization techniques that could reduce the processing time to under 10 minutes. For each optimization, explain the principle behind it, implement it in code, and measure the performance improvement. Include benchmarks before and after each optimization. Consider approaches like vectorization with NumPy, multiprocessing, memory mapping, JIT compilation with Numba, and algorithmic improvements. Provide a comprehensive report detailing your findings and final implementation.