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Scalability of modeling frameworks has become a major issue hampering MDE adoption in the industry. Specifically, scalable model persistence, as well as efficient query and transformation engines, are two of the key challenges that need to be addressed to enable the support for very large models in current applications. In this paper we demonstrate Mogwaï, a tool designed to efficiently compute queries and transformations (expressed in OCL and ATL) over models stored in NoSQL databases. Mogwaï relies on a translational approach that maps constructs of the supported input languages to Gremlin, a generic NoSQL query language, and a model to datastore mapping allowing to compute the generated query on top of several datastores. The produced queries are computed on the database side, benefiting of all its optimizations, improving the execution time and reducing the memory footprint …