One of the biggest challenges in SQL is keeping queries performant as data grows. A query that runs smoothly on a small test dataset can slow to a crawl when applied to millions of rows in production. In real-world projects, data professionals rely on many strategies: creating the right indexes, restructuring joins, breaking complex queries into smaller steps, and analyzing execution plans. Some swear by indexing, others by query refactoring, and some rely on caching results.

The real skill lies in knowing which approach to apply in which situation. For instance, adding indexes can drastically improve performance but may slow down inserts or updates. Refactoring queries can make them more readable and faster, but requires a deep understanding of the data. Execution plans reveal hidden bottlenecks, while caching results can save time for repeated analyses.

With so many options, it’s often a balance between query speed, maintainability, and accuracy. In your experience, what SQL optimization practices have been most effective in real-world projects? Which strategies do you rely on to make queries faster without compromising correctness? Share your insights and help the community learn from practical experiences.