The Neural Database framework, or NeuroDB for short, is a novel means of query answering using neural networks. It utilizes neural networks as a means of data storage by training neural networks to directly answer queries. That is, neural networks are trained to take queries as input and output query answer estimates. In doing so, relational tables are represented by neural network weights and are queried through a model forward pass. NeuroDB has shown significant practical advantages in (1) approximate query processing, (2) privacy-preserving query answering, and (3) querying incomplete datasets. The success of the NeuroDB framework can be attributed to the approach learning patterns present in the query answers, utilized to learn a compact representation of the dataset with respect to the queries. This allows learning small neural networks that accurately and efficiently represent query answers. Meanwhile, learning such patterns allows for improving the accuracy in the presence of error, with such robustness to noise allowing for improved accuracy in the case of private query answering and query answering on incomplete datasets. This paper presents an overview of the NeuroDB framework and its applications to the three aforementioned scenarios.