Data discovery is the process of discovering the metadata of source systems that include content, structure, patterns, and data domains. Content refers to data values, frequencies, and data types. Structure includes candidate keys, primary keys, foreign keys, and functional dependencies. The data discovery process offers advanced profiling capabilities.
In the native environment, you can define a profile to analyze data in a single data object or across multiple data objects. In the Hadoop environment, you can push column profiles and the data domain discovery process to the Hadoop cluster.
Run a profile to evaluate the data structure and to verify that data columns contain the types of information you expect. You can drill down on data rows in profiled data. If the profile results reveal problems in the data, you can apply rules to fix the result set. You can create scorecards to track and measure data quality before and after you apply the rules. If the external source metadata of a profile or scorecard changes, you can synchronize the changes with its data object. You can add comments to profiles so that you can track the profiling process effectively.