How do you handle performance when processing a large number of records?

When processing large volumes of records, I first assess the data flow to identify hotspots. I use batching or streaming to avoid loading everything into memory, processing chunks of, say, 10,000 rows at a time. I enable pagination or cursor-based retrieval to keep memory usage low. Indexes on filter columns reduce scan time, and I avoid N+1 query patterns by eager loading or joining. For CPU‑intensive transformations, I parallelize work across multiple threads or processes, ensuring thread safety. I also leverage caching for repeated lookups and consider in‑memory data stores like Redis for hot data. Monitoring tools (e.g., New Relic, Prometheus) help spot slow queries or memory leaks. Finally, I profile the code with a profiler to pinpoint bottlenecks and refactor or rewrite critical sections, ensuring the solution scales horizontally if needed.