Abstract: Data processing approaches are disclosed that include receiving a configuration indicating a plurality of parameters for performing a data processing job, identifying available compute resources from a plurality of public cloud infrastructures, where each public cloud infrastructure of the plurality of public cloud infrastructures supports one or more computing applications, one or more job schedulers, and one or more utilization rates, selecting one or more compute clusters from one or more of the plurality of public cloud infrastructures based on a matching process between the parameters for performing the data processing job and a combination of the one or more computing applications, the one or more job schedulers, and the one or more utilization rates, and initiating the one or more compute clusters for processing the data processing job based on the selecting.

Abstract: A method that includes receiving a first configuration and a second configuration that define a set of rules for matching and merging a set of source data objects that are associated with a tenant and that are received from a plurality of data sources. The method may further include generating a set of merged data objects from the set of source data objects based on an identification of matching values from fields of the set of source data objects and selecting a value for each field of each merged data object having multiple values. The method may further include generating a mapping between primary keys associated with each merged data object and corresponding primary keys of the source data objects. The method may further include storing the merged data objects and the mappings in a first datastore and a second datastore that is different from the first datastore.

Abstract: A method that includes receiving a first configuration and a second configuration that define a set of rules for matching and merging a set of source data objects that are associated with a tenant and that are received from a plurality of data sources. The method may further include generating a set of merged data objects from the set of source data objects based on an identification of matching values from fields of the set of source data objects and selecting a value for each field of each merged data object having multiple values. The method may further include generating a mapping between primary keys associated with each merged data object and corresponding primary keys of the source data objects. The method may further include storing the merged data objects and the mappings in a first datastore and a second datastore that is different from the first datastore.

Abstract: A method that includes receiving a first configuration and a second configuration that define a set of rules for matching and merging a set of source data objects that are associated with a tenant and that are received from a plurality of data sources. The method may further include generating a set of merged data objects from the set of source data objects based on an identification of matching values from fields of the set of source data objects and selecting a value for each field of each merged data object having multiple values. The method may further include generating a mapping between primary keys associated with each merged data object and corresponding primary keys of the source data objects. The method may further include storing the merged data objects and the mappings in a first datastore and a second datastore that is different from the first datastore.

Abstract: Data processing approaches are disclosed that include receiving a configuration indicating a plurality of parameters for performing a data processing job, identifying available compute resources from a plurality of public cloud infrastructures, where each public cloud infrastructure of the plurality of public cloud infrastructures supports one or more computing applications, one or more job schedulers, and one or more utilization rates, selecting one or more compute clusters from one or more of the plurality of public cloud infrastructures based on a matching process between the parameters for performing the data processing job and a combination of the one or more computing applications, the one or more job schedulers, and the one or more utilization rates, and initiating the one or more compute clusters for processing the data processing job based on the selecting.

Abstract: An enhanced pipeline for the generation, validation, and deployment of machine-based predictive models (PMs) is provided. The pipeline analyzes records to generate a graph that indicates various relationships between the records. A user provides a selection of a data element of interest (DEOI). The generated PM predicts values for the DEOI based on input records that do not include values for the DEOI. The user provides selections for values of the DEOI that represent positive outcomes associated with the DEOI. The user provides selections for values of the DEOI that represent negative outcomes associated with the DEOI. A subgraph of the graph is determined based on the DEOI. A relevant set of records is determined based on the subgraph. The PM is automatically trained, validated, and deployed based on the relevant set of records, the DEOI, and the representative values for the DEOI.