In many database applications, search is still executed via formbased query interfaces, which are then translated into SQL statements to find matching records. Ranking is usually not implemented unless users have explicitly indicated how to rank thematching records, e.g., in the ascending order of year. Often, this approach is neither intuitive nor user-friendly (especially with many search fields in a query form). It also requires application developers to design schema-specific query forms and develop specific user programs that understand these forms. In this work, we propose to demonstrate the ColumbuScout system that aims at quickly building and deploying a local search engine over one ormore large databases. The ColumbuScout system adopts a keyword-centric search approach. It integrates the keyword-centric principle with the latest results from approximate string search, and designs search-enginestyle ranking functions. It also introduces some of its own indexing structures and storage designs, to improve its overall efficiency and scalability. We will demonstrate that it is almost effortless for application developers to deploy ColumbuScout over any databases, and ColumbuScout is able to support search-engine-like types of search over large databases (more than 1.7 billion records in the examples we used) efficiently and effectively.