Balancing accuracy with performance in real-time data processing requires a mix of optimization strategies. One key approach is using indexing and partitioning to speed up queries while maintaining data integrity. Caching frequently accessed data also reduces load times. In a recent project involving a large hotel dataset in SSMS, I optimized slow-running CTE queries by breaking them into indexed temp tables, reducing execution time significantly. Prioritizing the right trade-offs—like pre-aggregating data where possible—ensures both speed and precision in decision-making.

Using as little dynamic memory as possible, especially in languages that allow you to do so like C/C++. You’d be surprised at how much of your allocation can be determined at compile time.