* The Bayesian Query Lanuage (BQL) allows analysts and domain experts to interact perform Bayesian data analysis without requiring a detailed understanding of model implementation. That means queries can be articulated before models have been build, and models can be improved and optimized without invalidating existing queries. * The Meta-modeling Language (MML) enables machine assisted modeling for populations based on samples and domain insight. By specifying population schemas and also by using the MML, domain experts can encode qualitative prior knowledge and control the behavior of BayesDB's built-in model building engine. * MML includes constructs for integrating arbitrary algorithmic models contained in external software, and for invoking a standard library of custom statistical modeling techniques. * BQL enables users to generate answers to a broad class of "what-if?" scenarios, contingencies and hypotheticals. These samples can be used as proxy data in sensitive settings, as the basis for model checking by domain experts, and as the basis for making complex risk-reward tradeoffs requiring full probability distributions on outcomes.

* Domain Model Flexibility - Shape domain models in a most suitable way, responding to needs and challenges as they come using built-in indexing capabilities * Perfect Audit Trail - Every event and its cause are serialized so that every action and its outcome can be traced * Functionality Mapping - Designating commands and events provides developers with a clear, succinct "one-page view" of available functionality * Strongly Typed Schema - All commands and events are compactly serialized according to their typing information and schema migration capabilities are included * Tooling - The Java implementation comes with JMX introspection and management primitives built-in. Additional application tooling is available, such as a GraphQL adaptor