Capital One Services, LLC

Timothy Haggerty

Yuting Zhou

Steven P Long

Venu Kumar Nannapaneni

Eric Henry

Deepa Rao

Pravin Nair

Hussein Ali Khalif Samao

Ramachandra Kancharla

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

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

Disclosed are systems, apparatuses and techniques for replicating data between different cloud computing platforms. Examples include storage replicator components operable in different cloud computing platforms. The first storage replicator component may identify the second cloud computing platform as a location to copy a data file in response to an event related to the data file stored in a first cloud computing platform. The first storage replicator component may request a copy of the data file via an application programming interface of the first cloud computing platform. The attributes of the copy of the data file which involve modification to conform to data management conventions of the second cloud computing platform may be determined and modified to comply with conventions of the second cloud computing platform. The modified copy of the data file may be forwarded to the second cloud computing platform for storage.

Multi-cloud bi-directional storage replication system and techniques

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Henrico, VA, United States of America

Timothy Haggerty