Progressive Visual Analytics (PVA) is meant to allow visual ana-lytics application to scale to large amounts of data while remaining interactive and steerable. The visualization community might believe that building progressive systems is difficult since there is no general purpose toolkit yet to build PVA applications, but it turns out that many existing libraries and data structures can be used effectively to help building PVA applications. They are just not well known by the visual analytics community. We report here on some of these techniques and libraries we use to handle "larger than RAM" data efficiently on three applications: Cartolabe, a system for visualizing large document corpora, ParcoursVis, a system for visualizing large event sequences from the French social security, and PPCA, a progressive PCA visualization system for large amounts of time-sequences. We explain how PVA can benefit from compressed bitset to manage sets internally and perform extremely fast Boolean operations , data sketching to compute approximate results over streaming data, and use Online algorithms to perform analyzes on large data.