The current developments in the research area of SQL processing and optimization are significantly advancing the field, particularly through innovative approaches that leverage large language models (LLMs) and novel intermediate representations. There is a notable shift towards more intuitive and efficient SQL-to-text generation models, which simplify SQL queries to align more closely with natural language, thereby enhancing the generation of text descriptions from SQL queries. Additionally, the integration of LLMs in query rewriting systems is addressing long-standing issues of robustness and accuracy in SQL optimization, by systematically guiding LLMs to avoid hallucinations and improve rewrite quality. Furthermore, the use of LLMs for query performance explanation in hybrid transactional and analytical processing (HTAP) systems is providing more accessible and insightful performance insights for users. These advancements are not only enhancing the efficiency and reliability of SQL processing but also making complex database operations more understandable and manageable for non-experts. Notably, the field is also witnessing progress in query performance prediction for high-performance OLAP systems, with new multi-stage methods that improve prediction precision. Overall, the trend is towards more sophisticated, yet user-friendly, tools that leverage advanced AI techniques to optimize and explain SQL operations.