Abstract: In some implementations, a device may obtain the biometric token associated with an account via a registration procedure associated with a first platform that is associated with a host. The server device may store an indication of the biometric token in a database. The server device may receive a login request associated with the account for a second platform that is associated with the host. The server device may identify that biometric authentication is enabled for the account based on the indication of the biometric token being stored in the database. The server device may provide an authentication challenge associated with the biometric token. The server device may receive a response to the authentication challenge indicating whether the authentication device has authenticated biometric information associated with a user based on the biometric token. The server device may authenticate the login request for the second platform based on the response.

Abstract: In some aspects, an apparatus includes a processor and a memory. In some embodiments, the memory includes programmed instructions that, when executed by the processor, cause the apparatus to receive a request to create a bucket. In some embodiments, the memory includes programmed instructions that, when executed by the processor, cause the apparatus to, in response to receiving the request to create the bucket, write, to a first object store, a first mapping that maps an identifier of global metadata to an identifier of a second object store where the bucket is to be created, write, to the second object store, a second mapping that maps the identifier of global metadata to local metadata, and replicate, to the second object store, the first mapping.

Abstract: Systems and methods for determining accurate addresses for assets are described herein. According to some aspects, a plurality of addresses associated with an asset are compiled from sources such as database systems. For the plurality of address records, scores that correspond to attributes of the address records can be determined. Attributes associated with the address records may include a recency, a source, a date, license plate data, or address type, to name several non-limiting examples. Additionally, a zone of interest for the asset can be determined based on the plurality of address records. The plurality of address records is prioritized based on their distance from the zone of interest and the scores associated with them and, based on the priority, a subset of the plurality of address records is sent to, for example, a service provider.

Abstract: A device may receive error data indicating that an error occurred, the error being associated with a data processing job scheduled to be performed by a data processing platform. The device may identify input data for the data processing job associated with the error and determine that the error is based on the data processing platform not receiving the input data. In addition, the device may determine a location of the input data and determine a measure of priority associated with the data processing job. Based on the location of the input data and the measure of priority, the device may perform an action to correct the error.

Abstract: Systems and methods for determining accurate addresses for assets are described herein. According to some aspects, a plurality of addresses associated with an asset are compiled from sources such as database systems. For the plurality of address records, scores that correspond to attributes of the address records can be determined. Attributes associated with the address records may include a recency, a source, a date, license plate data, or address type, to name several non-limiting examples. Additionally, a zone of interest for the asset can be determined based on the plurality of address records. The plurality of address records is prioritized based on their distance from the zone of interest and the scores associated with them and, based on the priority, a subset of the plurality of address records is sent to, for example, a service provider.

Abstract: A device receives, from a cloud computing environment, application usage information associated with application instances in the cloud computing environment for an application, and processes the application usage information, with a machine learning model, to determine behavior patterns and predicted tasks for the application. The device determines a modified quantity of the application instances based on the behavior patterns and the predicted tasks for the application, and causes the modified quantity of the application instances to be implemented in the cloud computing environment based on one or more rules. The device stores information associated with the modified quantity of the application instances in a data structure, and updates the machine learning model based on the information associated with the modified quantity of the application instances stored in the data structure.

Abstract: A device receives, from a cloud computing environment, application usage information associated with application instances in the cloud computing environment for an application, and processes the application usage information, with a machine learning model, to determine behavior patterns and predicted tasks for the application. The device determines a modified quantity of the application instances based on the behavior patterns and the predicted tasks for the application, and causes the modified quantity of the application instances to be implemented in the cloud computing environment based on one or more rules. The device stores information associated with the modified quantity of the application instances in a data structure, and updates the machine learning model based on the information associated with the modified quantity of the application instances stored in the data structure.

Abstract: A device may receive error data indicating that an error occurred, the error being associated with a data processing job scheduled to be performed by a data processing platform. The device may identify input data for the data processing job associated with the error and determine that the error is based on the data processing platform not receiving the input data. In addition, the device may determine a location of the input data and determine a measure of priority associated with the data processing job. Based on the location of the input data and the measure of priority, the device may perform an action to correct the error.

Abstract: A device receives, from a cloud computing environment, application usage information associated with application instances in the cloud computing environment for an application, and processes the application usage information, with a machine learning model, to determine behavior patterns and predicted tasks for the application. The device determines a modified quantity of the application instances based on the behavior patterns and the predicted tasks for the application, and causes the modified quantity of the application instances to be implemented in the cloud computing environment based on one or more rules. The device stores information associated with the modified quantity of the application instances in a data structure, and updates the machine learning model based on the information associated with the modified quantity of the application instances stored in the data structure.

Abstract: A device may receive error data indicating that an error occurred, the error being associated with a data processing job scheduled to be performed by a data processing platform. The device may identify input data for the data processing job associated with the error and determine that the error is based on the data processing platform not receiving the input data. In addition, the device may determine a location of the input data and determine a measure of priority associated with the data processing job. Based on the location of the input data and the measure of priority, the device may perform an action to correct the error.