Data profiling comprises a broad range of methods to efficiently analyze a given data set. In a typical scenario, which mirrors the capabilities of commercial data profiling tools, tables of a relational database are scanned to derive metadata, such as data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and occasionally functional dependencies and association rules. Individual research projects have proposed several additional profiling tasks, such as the discovery of inclusion dependencies or conditional functional dependencies.
The Metanome project is a project at HPI in cooperation with the Qatar Computing Reserach Institute (QCRI). Metanome provides a fresh view on data profiling by developing and integrating efficient algorithms into a common tool, expanding on the functionality of data profiling, and addressing performance and scalability issues for Big Data. A vision of the Metanome project appeared in SIGMOD Record "Data Profiling Revisited" and demo of the Metanome profiling tool was given at VLDB 2015 "Data Profiling with Metanome" (Please cite as BibTex/EndNote/ACM Ref) .