This paper presents a new family of Stata functions devoted to small area estimation. Small area methods attempt to solve low representativeness of surveys within areas, or the lack of data for specific areas/sub-populations. This is accomplished by incorporating information from outside sources. Such target data sets are becoming increasingly available and can take the form of a traditional population census, but also large scale administrative records from tax administrations, or geospatial information produced using remote sensing. The strength of these target data sets is their granularity on the subpopulations of interest, however, in many cases they lack the ability to collect analytically relevant variables such as welfare or caloric intake. The family of functions introduced follow a modular design to have the flexibility with which these can be expanded in the future. This can be accomplished by the authors and/or other collaborators from the Stata community. Thus far, a major limitation of such analysis in Stata has been the large size of target data sets. The package introduces new mata functions and a plugin used to circumvent memory limitations that inevitably arise when working with big data. From an estimation perspective, the paper starts by implementing a methodology that has been widely used for the production of several poverty maps.