Database management systems (DBMS) provide incredible flexibility and performance when it comes to query processing, scalability and accuracy. To fully exploit DBMS features, however, the user must define a schema, load the data, tune the system for the expected workload, and answer several questions. Should the database use a column-store, a row-store or some hybrid format? What indices should be created? All these questions make for a formidable and time-consuming hurdle, often deterring new applications or imposing high cost to existing ones. A characteristic example is that of scientific databases with huge data sets. The prohibitive initialization cost and complexity still forces scientists to rely on "ancient" tools for their data management tasks, delaying scientific understanding and progress. Users and applications collect their data in flat files, which have traditionally been considered to be "outside" a DBMS. A DBMS wants control: always bring all data "inside", replicate it and format it in its own "secret" way. The problem has been recognized and current efforts extend existing systems with abilities such as reading information from flat files and gracefully incorporating it into the processing engine. This paper proposes a new generation of systems where the only requirement from the user is a link to the raw data files. Queries can then immediately be fired without preparation steps in between. Internally and in an abstract way, the system takes care of selectively, adaptively and incrementally providing the proper environment given the queries at hand. Only part of the data is loaded at any given time and it is being stored and accessed in the format suitable for the current workload.