Scalable data profiling : distributed discovery and analysis of structural metadata Data profiling is the act of extracting structural metadata from datasets. Structural metadata such as data dependencies and statistics can support data management operations such as data integration and data cleaning. Data management often is the most time consuming activity in any data related project. Its support is extremely valuable in our data driven world so that more time can be spent on the actual utilization of the data e. g. building analytical models. In most scenarios however structural metadata is not given and must be extracted first. Therefore efficient data profiling methods are highly desirable. Data profiling is a computationally expensive problem; in fact most dependency discovery problems entail search spaces that grow exponentially in the number of attributes. To this end this thesis introduces novel discovery algorithms for various types of data dependencies – namely inclusion dependencies conditional inclusion dependencies partial functional dependencies and partial unique column combinations – namely inclusion dependencies conditional inclusion dependencies partial functional dependencies and partial unique column combinations – that considerably improve over state of the art algorithms in terms of efficiency and that scale to datasets that cannot be processed by existing algorithms. [...]