You're tasked with optimizing a data processing application that currently handles approximately 1 million records per hour but needs to scale to 10 million records per hour without increasing hardware costs. The application performs complex transformations, aggregations, and machine learning inference on each record. Provide a detailed optimization strategy covering algorithmic improvements, data structure choices, caching strategies, parallelization approaches, and memory management. Discuss profiling techniques to identify bottlenecks and how to measure the effectiveness of each optimization. Explain the trade-offs of your proposed approaches.