Recommender systems have become popular in both commercial and academic settings. The main purpose of recommender systems is to suggest to users useful and interesting items or content (data) from a considerably large set of items. Traditional recommender systems do not take into account system issues (i.e., scalability and query efficiency). In an age of staggering web use growth and everpopular social media applications (e.g., Facebook, Google Reader), users are expressing their opinions over a diverse set of data (e.g., news stories, Facebook posts, retail purchases) faster than ever. In this paper, we propose RecDB; a fully fledged database system that provides online recommendation to users. We implement RecDB using existing open source database system Apache Derby, and we use showcase the effectiveness of RecDB by adopting inside Sindbad; a Location-Based Social Networking system developed at University of Minnesota.