As data centric approach, Business Intelligence (BI) deals with the storage, integration, processing, exploration and analysis of information gathered from multiple sources in various for-mats and volumes. BI systems are generally synonymous to costly, complex platforms that re-quire vast organizational resources. But there is also an-other face of BI, that of a pool of data sources, applications, services developed at different times using different technologies. This is "democratic" BI or, in some cases, "fragmented", "patched" (or "chaotic") BI. Fragmentation creates not only integration problems, but also supports BI agility as new modules can be quickly developed. Among various languages and tools that cover large extents of BI activities, SQL and R are instrumental for both BI... platform developers and BI users. SQL and R address both monolithic and democratic BI. This paper compares essential data processing features of two languages, identifying similarities and differences among them and also their strengths and limits.