Data profiling comprises a broad range of methods to efficiently extract various metadata from a given dataset, including data types and value patterns, completeness and uniqueness of columns, keys and foreign keys, and various other data dependencies. The talk highlights the key insights behind recent state of the art methods and presents various use cases in the areas of data cleaning and data integration: violations of dependencies point to errors in the data; key discovery identifies the core entities of a data source; inclusion dependencies are candidates to join up multiple sources; and in general, data profiling results can be used to organize data lakes.