The field of text-to-SQL and data engineering is moving towards more advanced and innovative solutions. Recent research has focused on improving the accuracy and efficiency of text-to-SQL models, with a particular emphasis on handling multiple SQL dialects and complex schema. Additionally, there is a growing trend towards utilizing large language models to improve data engineering tasks, such as data processing and query generation. Noteworthy papers in this area include ExeSQL, which introduces a novel framework for text-to-SQL models that can adapt to new SQL dialects through verifiable, feedback-guided learning. UNJOIN is another notable paper that proposes a two-stage framework for multi-table text-to-SQL generation via schema simplification. StreamLink is also a significant contribution, introducing an LLM-driven distributed data engineering system that improves the efficiency and accessibility of data engineering tasks. Other notable papers include GXJoin, Knowledge Base Construction for Knowledge-Augmented Text-to-SQL, TabXEval, TailorSQL, LINEAGEX, and Map&Make, each of which presents innovative solutions to various challenges in the field.