The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Method and system for training a machine learning model based on a training dataset formed by data objects distributed across a virtual object storage service. The method comprises fetching from the virtual object storage service, the training dataset; copying the fetched training dataset on a first local storage device and maintaining a list of modifications executed on the training dataset that occurred on the virtual object storage service. The method comprises, upon receiving a request to initiate training of the machine learning model, generating a synchronized training dataset mirroring the training dataset stored in the virtual object storage service; storing the synchronized training dataset in a second local storage device; and fetching training data from the synchronized training dataset stored in the second local storage device as the training of the machine learning model is executed.

Systems and methods for synchronizing data between a customer data management system and a data warehouse system. A data warehouse server may constantly monitor a dynamic metadata flow from the customer data management system, compare it with the metadata in the data storage device, and dynamically update the metadata in the data storage device. The data warehouse server may track activities over time and accumulate a long running history, which may include multiple versions of accounts in the customer data management system, e.g., the account as of today, the account as of yesterday, and another version that was the account two weeks ago.

Systems and methods for synchronizing data between a customer data management system and a data warehouse system. A data warehouse server may constantly monitor a dynamic metadata flow from the customer data management system, compare it with the metadata in the data storage device, and dynamically update the metadata in the data storage device. The data warehouse server may track activities over time and accumulate a long running history, which may include multiple versions of accounts in the customer data management system, e.g., the account as of today, the account as of yesterday, and another version that was the account two weeks ago.

An Orchestrated Data Recovery (ODR) Cyber Protection Automation (CPA) operates to ensure one-to-one creation of snapsets of a production site and corresponding snapsets of a cyber vault. During an initiation phase, the ODR CPA monitors synchronization of a snapset of production volumes from the production site to the cyber vault. If additional snapsets of the production volumes are created prior to completion of synchronization of the first snapset, the additional snapsets are also synchronized to the cyber vault. Once the initial synchronization of the storage volumes has been completed, the ODR CPA causes a Storage Volume Creation and Management System (SVCMS) to create a snapset of the storage volumes at the cyber vault. Subsequently, each time a snapset is created of the production site, the ODR CPA orchestrates synchronization of the snapset to the cyber vault and creation of a corresponding snapset at the cyber vault.

An Orchestrated Data Recovery (ODR) Cyber Protection Automation (CPA) operates to ensure one-to-one creation of snapsets of a production site and corresponding snapsets of a cyber vault. During an initiation phase, the ODR CPA monitors synchronization of a snapset of production volumes from the production site to the cyber vault. If additional snapsets of the production volumes are created prior to completion of synchronization of the first snapset, the additional snapsets are also synchronized to the cyber vault. Once the initial synchronization of the storage volumes has been completed, the ODR CPA causes a Storage Volume Creation and Management System (SVCMS) to create a snapset of the storage volumes at the cyber vault. Subsequently, each time a snapset is created of the production site, the ODR CPA orchestrates synchronization of the snapset to the cyber vault and creation of a corresponding snapset at the cyber vault.

In embodiments, application data is received from multiple application platforms. Once received, an object mapping is used to determine a synchronization data object that is mapped to data objects from the application data. A function mapping is used to determine one or more functions to apply to the application data. Data objects from the application data that are mapped to the synchronization data object are compared to detect conflicts between fields. In response to detecting a conflict, the conflict is resolved based on configuration rules that indicate priority between fields of different data objects. Once the conflict is resolved, the data objects are merged into a modified synchronization data object that represents an updated version of the synchronization data object. Changes between the synchronization data object and the modified synchronization data object are identified and propagated to application platforms to perform updates to application data managed by the application platforms.

In embodiments, application data is received from multiple application platforms. Once received, an object mapping is used to determine a synchronization data object that is mapped to data objects from the application data. A function mapping is used to determine one or more functions to apply to the application data. Data objects from the application data that are mapped to the synchronization data object are compared to detect conflicts between fields. In response to detecting a conflict, the conflict is resolved based on configuration rules that indicate priority between fields of different data objects. Once the conflict is resolved, the data objects are merged into a modified synchronization data object that represents an updated version of the synchronization data object. Changes between the synchronization data object and the modified synchronization data object are identified and propagated to application platforms to perform updates to application data managed by the application platforms.

A method of summarizing data files includes implementing, at a server, a storage event for a data file, analyzing the data file and creating a summary of the data file, and storing the summary linked to the data file.