Vertica offers a high-performance analytics engine that is comfortable performing analytics as a data warehouse and running fast mass insert/load transactions. In this session, we discuss the nuances of the analytical and transactional dynamics of Vertica. How does Vertica’s specific transactional model help in case of mass data transactions? Why can we avoid having to maintain indexes, as when row-based can’t avoid it? Why can we avoid the dreaded long rollback in case data errors that row-based databases have to deal with? How do our large analytical function toolbox, database designer, and external tables features all work together to help you achieve OLAP excellence?