Imagine asking complex questions about data in a table and instantly receiving accurate answers. Researchers are exploring innovative ways to make this a reality by combining different AI-powered approaches to Table Question Answering (TQA). Traditionally, TQA models either translate questions into structured queries (Text-to-SQL) or directly predict answers (E2E TQA). However, both methods have limitations. Text-to-SQL excels at numerical reasoning and handling large tables, but struggles with ambiguities and complex table layouts. E2E TQA is more adaptable to these challenges, but often falls short on precise numerical calculations. In a new approach called SynTQA, researchers are combining the strengths of both methods. This synergistic strategy uses AI to intelligently select the best answer from both the Text-to-SQL and E2E TQA models, effectively boosting overall accuracy. Tests show SynTQA significantly outperforms individual models, marking a promising advancement for TQA. This breakthrough opens doors to numerous applications. Imagine business analysts quickly gleaning insights from sales data, researchers effortlessly navigating complex datasets, or even everyday users easily extracting information from online tables. While challenges remain, the potential of SynTQA to revolutionize data interaction is clear. As research continues, we can expect increasingly sophisticated TQA systems capable of unlocking even deeper insights from the vast ocean of tabular data that surrounds us.