Abstract: Disclosed herein are system, method, and computer program product embodiments for generating metadata element recommendations. For example, the method includes acquiring, by at least one processor and via a user interface, metadata associated with a data store. The method also includes performing natural language processing on the metadata to generate processed metadata, generating a candidate table name and a table description associated with the candidate table name for a table included in the metadata, and generating a first candidate attribute name, an attribute description associated with the first candidate attribute name, and a corresponding data type for each attribute associated with the table. The method also includes generating a second candidate attribute name for each attribute by extracting one or more keywords from the first candidate attribute name, and modifying the user interface to include at least the candidate table name.

Abstract: Disclosed are various embodiments for data anomaly detection. A variable profile is generated for each variable in source data. Then, the variable profiles are provided to each of a plurality of machine learning models. Next, it is determined, with each of the plurality of machine learning models, whether each variable profile is anomalous. The determination, from each of the plurality of machine learning models, whether each variable profile is anomalous is provided to an ensemble model. The ensemble model then generates a final determination whether each variable profile is anomalous. The final determination is then reported to an analysis service.

Abstract: Disclosed are various approaches performing distributed profiling and validation. A query is generated for profile data for data stored in a data warehouse. Then, the query for the profile data is sent to the data warehouse. The profile data is then received from the data warehouse. Later, in response to a request to load the data stored in the data warehouse into a table in a data store, a determination is made as to whether the data stored in the data warehouse complies with a validation rule.

Abstract: Disclosed are various embodiments for data anomaly detection. A variable profile is generated for each variable in source data. Then, the variable profiles are provided to each of a plurality of machine learning models. Next, it is determined, with each of the plurality of machine learning models, whether each variable profile is anomalous. The determination, from each of the plurality of machine learning models, whether each variable profile is anomalous is provided to an ensemble model. The ensemble model then generates a final determination whether each variable profile is anomalous. The final determination is then reported to an analysis service.