Capital One Services, LLC

Venu Kumar Nannapaneni

Timothy Haggerty

Ramachandra Kancharla

Pravin Nair

Sarvani Kare

Yuting Zhou

Ada Musheyev

Yifan Guo

Hussein Ali Khalif Samao

Ganesh Kandula

Priyank Chittaranjan Subhedar

Techniques for performing parallel processing jobs across regionally-distributed servers may include executing a requested job in parallel across two or more regional cloud-based servers, the status of parallel jobs may be locally monitored using respective local instances of a status-tracking database. The multiple instances may replicate modifications of one instance of the status database to other instances of the status database to synchronize the status of regional jobs. Replicating modifications across the respective regional instances of the status database facilitates the parallel executing of the jobs across regions while reducing or avoiding unrecoverable delays if the tasks/job hangs or otherwise fails.

Multi-region workflow management

In some implementations, a labeling device may determine a label value associated with a label of the document that is associated with a first application. The labeling device may generate a label map that includes an indication of a key-value pair associated with the label value. The labeling device may write, based on the key-value pair associated with the label value, the label value to one or more file properties associated with the document, or may add, based on the key-value pair associated with the label value, the label value as content associated with the document. The label value may persist with the document based on transferring the document from the first application to a second application.

Automated persistence of label information associated with a document

Data queries that are agnostic to any particular data source may include a data source alias. The data source alias may be replaced with a data source identifier to obtain a data query configured for a target data source. Data processing jobs may be agnostic to any particular data processing platform. A data processing job may include a data processing task that is agnostic to any particular data processing platform. A code library may provide platform-specific code configured to implement a data processing task on a data processing platform. A data query configured for a particular data source and a data processing task configured for a particular data processing platform may be used to create a data processing job. Configurations that restrict execution of a data processing job to execution via an interactive development environment may be removed to allow its execution directly at the data processing platform itself.

Platform and source agnostic data processing for structured and unstructured data sources

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Glen Allen, VA, United States of America

Venu Kumar Nannapaneni