To fully support the analysis of complex and structured data, new efficient computational methods and suitable interfaces for data exploration have to be developed. Moreover, it is desirable to perform all tasks in the knowledge discovery process, from pre-processing to post-processing, on the basis of query languages. Inductive query languages should allow handling patterns/models as first-class objects, provide the right level of abstraction to the user (i.e., meaningful building blocks of data analysis), and emphasize the compositionality of data mining tasks. In a major development and implementation effort, we created a research prototype of a working inductive database, SINDBAD (Structured Inductive Database Development), to explore research topics in the context of data mining query languages and inductive databases. SINDBAD is built on top of a relational database management system, offers an SQL extension for data pre-processing, mining, and post-processing, and achieves closure by successive transformation of tables.