Abstract: In some implementations, a system may receive inventory data associated with a data storage system. The inventory data identifies file paths for objects stored in the data storage system. The system may detect patterns in prefixes of the file paths using one or more trained machine learning models. The system may normalize the prefixes of the file paths based on the patterns detected in the prefixes. The system may detect datasets of the objects stored in the data storage system based on the normalized prefixes. The system may compare prefixes associated with the detected datasets with prefixes associated with a set of registered datasets that are registered with a metadata repository. The system may determine, based on comparing the prefixes associated with the detected datasets and the prefixes associated with the set of registered datasets, a respective registration classification for each detected dataset.

Abstract: Systems and methods are disclosed herein for providing contextually relevant incentives in real-time to consumers. In one example, the method may include receiving, from a data repository, one or more raw data streams of real time data and determining an identity of the end user based on the one or more raw data streams. The method may further include filtering, using contextual filtering parameters, the received one or more raw data streams to produce filtered data. The method may also include applying a set of rules correlating the filtered data with incentive programs relevant to the filtered data to generate correlated data, and querying the data repository for historical data associated with the end user. The method may also include calculating a propensity score of the end user, and ranking the correlated data. The method may also include sending one or more incentives based on the correlated data ranking.

Abstract: Disclosed herein are system, method, and computer program product embodiments for performing in-flight transformations of data streams. Complex event processing (CEP) operating on event records from various data streams can operate by storing the streamed event record data in a database, and then querying the data to perform a data transformation. However, in order to improve the performance of the data transformations and streamline CEP, a serverless architecture is introduced that can perform data transformations directly on streams, using attributes of the streamed record data defined in a schema. The resulting transformed data can then be provided by the serverless architecture to the CEP for direct access of the data most needed by the CEP.