The field of text-to-SQL and data discovery is moving towards more innovative and adaptive approaches. Recent developments have focused on leveraging large language models (LLMs) to improve query generation and rewriting. There is also a growing interest in developing more expressive and flexible query languages that can handle complex queries and multiple database schemas. Noteworthy papers in this area include: End-to-End Text-to-SQL with Dataset Selection, which proposes a three-stage framework for identifying the user's intended database before generating SQL queries. E3-Rewrite, an LLM-based SQL rewriting framework that produces executable, equivalent, and efficient queries, achieving up to a 25.6% reduction in query execution time compared to state-of-the-art methods. TQL, a domain-specific language for data discovery that incorporates a type-like system to encompass downstream transformation-context in its discovery queries, providing a more expressive and practical approach to data discovery.