Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (Saas) applications may host customers'information in a distributed environment that is not directly accessible to the customers. A data management system (DMS) may manage data backup and restoration for a SaaS application and may use dynamic data access objects (DAOs). The DMS may detect the schema of a table retrieved from a SaaS application via an application programming interface (API) and convert the data in the table to a dynamic DAO based on the detected schema. The dynamic DAO may subsequently be converted to an object for storage in a storage environment. To restore data to a SaaS application, the DMS may detect the expected schema for the SaaS application, convert stored data to a dynamic DAO based on the expected schema, and restore the data from the dynamic DAO to the SaaS application via an API.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables, meaning that data is organized in tables that have hierarchical relationships. SaaS tables may include permissive relationships to other tables (e.g., a row in one table may point to data in another table). Restoration of a SaaS computing object (e.g., one or more SaaS tables) may involve restoration of other computing objects in both dependent and permissive relationships with the SaaS computing object. Cascading metadata logic may be stored in association with a particular computing object that may indicate, for each relationship, whether to perform a restore operation and a type of cascading restore to perform when the particular computing object is to be restored to a particular computing environment.

Abstract: Systems and methods for cloud object storage and versioning are provided. In an example, a cloud object storage and versioning system (COSVS) coordinates insertion of metadata into a database and data into an object store in a compute infrastructure, the compute infrastructure including the database, the object store, a data source, and a client application interacting with the data source. The COSVS has an architecture comprising a client API layer confined to provide an interface to the client application to facilitate storing or restoring of items originally obtained from the data source, and query different versions of item content originally obtained from the data source; a deduplication layer to deduplicate item content and store items originally obtained from the data source devoid of external item metadata or semantic relations among items; and a packing layer confined to store and retrieve bytes of item or object content.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables, meaning that data is organized in tables that have hierarchical relationships. SaaS tables may include permissive relationships to other tables (e.g., a row in one table may point to data in another table). Restoration of a SaaS computing object (e.g., one or more SaaS tables) may involve restoration of other computing objects in both dependent and permissive relationships with the SaaS computing object. Cascading metadata logic may be stored in association with a particular computing object that may indicate, for each relationship, whether to perform a restore operation and a type of cascading restore to perform when the particular computing object is to be restored to a particular computing environment.

Abstract: A data management system (DMS) may support the backing up of scalable computing objects, such as hierarchical computing objects. For example, the DMS may backup a computing system and facilitate that capture and storage of snapshots of the computing system. The DMS may determine to capture a first snapshot of a computing object within the computing system. The DMS may identify components corresponding to respective portions of the computing object and cause the computing system to generate respective snapshots of the components. That is, the DMS may cause the computing system to generate multiple individual snapshots that together may constitute snapshot of the computing object. The DMS may generate the first snapshot that includes metadata for the snapshots of the components. The metadata may include a first identifier for the first snapshot and respective identifiers for the respective snapshots of the components.

Abstract: Systems and methods for cloud object storage and versioning are provided. In an example, a cloud object storage and versioning system (COSVS) coordinates insertion of metadata into a database and data into an object store in a compute infrastructure, the compute infrastructure including the database, the object store, a data source, and a client application interacting with the data source. The COSVS has an architecture comprising a client API layer confined to provide an interface to the client application to facilitate storing or restoring of items originally obtained from the data source, and query different versions of item content originally obtained from the data source; a deduplication layer to deduplicate item content and store items originally obtained from the data source devoid of external item metadata or semantic relations among items; and a packing layer confined to store and retrieve bytes of item or object content.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables. Snapshots for SaaS applications may maintain hierarchical relationships between computing objects and tables to enable cascading recovery. A data management system (DMS) may identify the computing object hierarchy for an organization's data as hosted by a SaaS application and also may identify the APIs associated with accessing each computing object, which may each include multiple tables. Tables that are accessed via a same API may be organized as logical entities. The DMS may store the hierarchical relationship between the tables, and as the tables are stored as logical entities, the DMS may organize the tables in the backup database based on which APIs are used to access the relevant tables.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers information in a distributed environment that is not directly accessible to the customers. In some SaaS applications each organization may have a settings computing object, a features computing object, and a set of projects computing objects. The hierarchical relationships in each of the projects computing objects may depend on the tables in the settings and features computing objects. A data management system (DMS) may include discovery, backup, refresh, and restore frameworks that maintain hierarchical relationships between computing objects and tables including the dependency of projects computing objects on settings or features computing objects.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables. Snapshots for SaaS applications may maintain hierarchical relationships between computing objects and tables to enable cascading recovery. A data management system (DMS) may identify the computing object hierarchy for an organization's data as hosted by a SaaS application and also may identify the APIs associated with accessing each computing object, which may each include multiple tables. Tables that are accessed via a same API may be organized as logical entities. The DMS may store the hierarchical relationship between the tables, and as the tables are stored as logical entities, the DMS may organize the tables in the backup database based on which APIs are used to access the relevant tables.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers information in a distributed environment that is not directly accessible to the customers. In some SaaS applications each organization may have a settings computing object, a features computing object, and a set of projects computing objects. The hierarchical relationships in each of the projects computing objects may depend on the tables in the settings and features computing objects. A data management system (DMS) may include discovery, backup, refresh, and restore frameworks that maintain hierarchical relationships between computing objects and tables including the dependency of projects computing objects on settings or features computing objects.

Abstract: A data management system (DMS) may use a staggered full snapshot rollout approach to obtain full snapshots of multiple computing objects. The DMS may identify that a quantity of the multiple objects scheduled for full backups by the DMS exceeds a threshold quantity. The DMS may initiate, at a first time, and based on the quantity of objects exceeding the threshold quantity, a first set of full snapshots corresponding to a first subset of objects included in the multiple objects. The DMS may initiate, at a second time subsequent to the first time, a second set of full snapshots corresponding to a second subset of objects included in the multiple objects. The second subset of objects may be different than the first subset of objects. The DMS may continue to obtain full snapshots of respective subsets of the multiple objects until all of the scheduled full snapshots are obtained.

Abstract: A data management system (DMS) may use a staggered full snapshot rollout approach to obtain full snapshots of multiple computing objects. The DMS may identify that a quantity of the multiple objects scheduled for full backups by the DMS exceeds a threshold quantity. The DMS may initiate, at a first time, and based on the quantity of objects exceeding the threshold quantity, a first set of full snapshots corresponding to a first subset of objects included in the multiple objects. The DMS may initiate, at a second time subsequent to the first time, a second set of full snapshots corresponding to a second subset of objects included in the multiple objects. The second subset of objects may be different than the first subset of objects. The DMS may continue to obtain full snapshots of respective subsets of the multiple objects until all of the scheduled full snapshots are obtained.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers information in a distributed environment that is not directly accessible to the customers. In some SaaS applications each organization may have a settings computing object, a features computing object, and a set of projects computing objects. The hierarchical relationships in each of the projects computing objects may depend on the tables in the settings and features computing objects. A data management system (DMS) may include discovery, backup, refresh, and restore frameworks that maintain hierarchical relationships between computing objects and tables including the dependency of projects computing objects on settings or features computing objects.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host their customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables, meaning that data is organized in tables that have hierarchical parent and child relationships. Snapshots for SaaS applications may maintain hierarchical relationships between computing objects and tables to enable cascading recovery of SaaS applications. Based on a request to restore a computing object of a relational SaaS application, a data management system (DMS) may identify the associated computing objects to be recovered with the requested computing object based on the stored hierarchical information, and may call the relevant application programming interfaces (APIs) to restore the requested computing object and the associated computing objects to be recovered with the requested computing object.

Abstract: Methods, systems, and devices for data management are described. Software-as-a-service (SaaS) applications may host customers' information in a distributed environment that is not directly accessible to the customers. SaaS applications may store data in the form of relational tables. Snapshots for SaaS applications may maintain hierarchical relationships between computing objects and tables to enable cascading recovery. A data management system (DMS) may identify the computing object hierarchy for an organization's data as hosted by a SaaS application and also may identify the APIs associated with accessing each computing object, which may each include multiple tables. Tables that are accessed via a same API may be organized as logical entities. The DMS may store the hierarchical relationship between the tables, and as the tables are stored as logical entities, the DMS may organize the tables in the backup database based on which APIs are used to access the relevant tables.

Abstract: A data management system (DMS) may support the backing up of scalable computing objects, such as hierarchical computing objects. For example, the DMS may backup a computing system and facilitate that capture and storage of snapshots of the computing system. The DMS may determine to capture a first snapshot of a computing object within the computing system. The DMS may identify components corresponding to respective portions of the computing object and cause the computing system to generate respective snapshots of the components. That is, the DMS may cause the computing system to generate multiple individual snapshots that together may constitute snapshot of the computing object. The DMS may generate the first snapshot that includes metadata for the snapshots of the components. The metadata may include a first identifier for the first snapshot and respective identifiers for the respective snapshots of the components.

Abstract: A data management system (DMS) may support the backing up of scalable computing objects, such as hierarchical computing objects. For example, the DMS may backup a computing system and facilitate that capture and storage of snapshots of the computing system. The DMS may determine to capture a first snapshot of a computing object within the computing system. The DMS may identify components corresponding to respective portions of the computing object and cause the computing system to generate respective snapshots of the components. That is, the DMS may cause the computing system to generate multiple individual snapshots that together may constitute snapshot of the computing object. The DMS may generate the first snapshot that includes metadata for the snapshots of the components. The metadata may include a first identifier for the first snapshot and respective identifiers for the respective snapshots of the components.

Abstract: A data management system (DMS) may use a staggered full snapshot rollout approach to obtain full snapshots of multiple computing objects. The DMS may identify that a quantity of the multiple objects scheduled for full backups by the DMS exceeds a threshold quantity. The DMS may initiate, at a first time, and based on the quantity of objects exceeding the threshold quantity, a first set of full snapshots corresponding to a first subset of objects included in the multiple objects. The DMS may initiate, at a second time subsequent to the first time, a second set of full snapshots corresponding to a second subset of objects included in the multiple objects. The second subset of objects may be different than the first subset of objects. The DMS may continue to obtain full snapshots of respective subsets of the multiple objects until all of the scheduled full snapshots are obtained.

Abstract: A data management system (DMS) may support the backing up of scalable computing objects, such as hierarchical computing objects. For example, the DMS may backup a computing system and facilitate that capture and storage of snapshots of the computing system. The DMS may determine to capture a first snapshot of a computing object within the computing system. The DMS may identify components corresponding to respective portions of the computing object and cause the computing system to generate respective snapshots of the components. That is, the DMS may cause the computing system to generate multiple individual snapshots that together may constitute snapshot of the computing object. The DMS may generate the first snapshot that includes metadata for the snapshots of the components. The metadata may include a first identifier for the first snapshot and respective identifiers for the respective snapshots of the components.

Abstract: Systems and methods for cloud object storage and versioning are provided. In an example, a cloud object storage and versioning system (COSVS) coordinates insertion of metadata into a database and data into an object store in a compute infrastructure, the compute infrastructure including the database, the object store, a data source, and a client application interacting with the data source. The COSVS has an architecture comprising a client API layer confined to provide an interface to the client application to facilitate storing or restoring of items originally obtained from the data source, and query different versions of item content originally obtained from the data source; a deduplication layer to deduplicate item content and store items originally obtained from the data source devoid of external item metadata or semantic relations among items; and a packing layer confined to store and retrieve bytes of item or object content.