Abstract: One example method includes receiving from a node, in an HSAN that includes multiple nodes, an ADD_DATA request to add an entry to a distributed ledger of the HSAN, the request comprising a user ID that identifies the node, a hash of a data segment, and a storage location of the data segment at the node, performing a challenge-and-response process with the node to verify that the node has a copy of the data that was the subject of the entry, making a determination that a replication factor X has not been met, and adding the entry to the distributed ledger upon successful conclusion of the challenge-and-response process.

Abstract: One example method includes defining object groups by classifying each object in a backup saveset based on respective object types of the objects such that all objects in an object group are the same object type, assigning a different respective storage media type to each of the object groups, storing each object group at a respective storage target, representing each object group with a respective Merkle tree that includes a base hash, and mapping each base hash to the storage target where the object group associated with the Merkle tree that includes the base hash is stored.

Abstract: One example method includes receiving, from an entity, a proposed entry for a ledger, where the ledger is shared and accessible by multiple users and includes a whitelist and a blacklist, determining, or assigning, a credibility score and rate limiter value for the entity, comparing the credibility score and rate limiter value with respective credibility score and rate limiter value thresholds, determining that the credibility score and rate limiter value meet or exceed the respective credibility score and rate limiter value thresholds, and submitting the proposed entry to the ledger.

Abstract: One example method includes receiving from a node, in an HSAN that includes multiple nodes, an ADD_DATA request to add an entry to a distributed ledger of the HSAN, the request comprising a user ID that identifies the node, a hash of a data segment, and a storage location of the data segment at the node, performing a challenge-and-response process with the node to verify that the node has a copy of the data that was the subject of the entry, making a determination that a replication factor X has not been met, and adding the entry to the distributed ledger upon successful conclusion of the challenge-and-response process.

Abstract: One example method includes receiving from a node, in an HSAN that includes multiple nodes, an ADD_DATA request to add an entry to a distributed ledger of the HSAN, the request comprising a user ID that identifies the node, a hash of a data segment, and a storage location of the data segment at the node, performing a challenge-and-response process with the node to verify that the node has a copy of the data that was the subject of the entry, making a determination that a replication factor X has not been met, and adding the entry to the distributed ledger upon successful conclusion of the challenge-and-response process.

Abstract: One example method includes receiving a group of files, two or more of the files being of different respective file types, creating a backup saveset that includes the group of files, classifying each of the files in the backup saveset based in part on the respective file types of the files, assigning a respective storage media type to each of the classified files in the backup saveset, and transmitting the backup saveset to a storage site.

Abstract: One example method includes receiving an input that indicates selection of a cloud storage provider, receiving one or more product selection inputs, each of the product selection inputs indicating selection of a respective data protection product, receiving an input indicating an instance size, assembling the inputs together to define a data protection configuration, and automatically generating a script which, when executed by one or more hardware processors, deploys the data protection configuration in a cloud storage environment of the selected cloud storage provider.

Abstract: Described is a system for a Policy Derivation Engine (PDE) that, when accessed by one or more organizations, assists such organizations in updating and managing data protection policies based on external policy updates from one or more other organizations that share the same organizational peer group. The system receives a policy request from a first computing system associated with a first organization. The system computes a list of peer organizations of the first organization. The system builds policy update data based on respective policy data received from one or more peer organizations and transmits at least a portion of policy update data to the first computing system associated with the first organization.

Abstract: One example method includes receiving an input that indicates selection of a cloud storage provider, receiving one or more product selection inputs, each of the product selection inputs indicating selection of a respective data protection product, receiving an input indicating an instance size, assembling the inputs together to define a data protection configuration, and automatically generating a script which, when executed by one or more hardware processors, deploys the data protection configuration in a cloud storage environment of the selected cloud storage provider.

Abstract: One example method includes receiving an input that indicates selection of a cloud storage provider, receiving one or more product selection inputs, each of the product selection inputs indicating selection of a respective data protection product, receiving an input indicating an instance size, assembling the inputs together to define a data protection configuration, and automatically generating a script which, when executed by one or more hardware processors, deploys the data protection configuration in a cloud storage environment of the selected cloud storage provider.

Abstract: One example method includes defining object groups by classifying each object in a backup saveset based on respective object types of the objects such that all objects in an object group are the same object type, assigning a different respective storage media type to each of the object groups, storing each object group at a respective storage target, representing each object group with a respective Merkle tree that includes a base hash, and mapping each base hash to the storage target where the object group associated with the Merkle tree that includes the base hash is stored.

Abstract: One example method includes receiving an input that indicates selection of a cloud storage provider, receiving one or more product selection inputs, each of the product selection inputs indicating selection of a respective data protection product, receiving an input indicating an instance size, assembling the inputs together to define a data protection configuration, and automatically generating a script which, when executed by one or more hardware processors, deploys the data protection configuration in a cloud storage environment of the selected cloud storage provider.

Abstract: One example method includes receiving an input that indicates selection of a cloud storage provider, receiving one or more product selection inputs, each of the product selection inputs indicating selection of a respective data protection product, receiving an input indicating an instance size, assembling the inputs together to define a data protection configuration, and automatically generating a script which, when executed by one or more hardware processors, deploys the data protection configuration in a cloud storage environment of the selected cloud storage provider.

Abstract: One example method includes receiving from a node, in an HSAN that includes multiple nodes, an ADD_DATA request to add an entry to a distributed ledger of the HSAN, the request comprising a user ID that identifies the node, a hash of a data segment, and a storage location of the data segment at the node, performing a challenge-and-response process with the node to verify that the node has a copy of the data that was the subject of the entry, making a determination that a replication factor X has not been met, and adding the entry to the distributed ledger upon successful conclusion of the challenge-and-response process.

Abstract: One example method includes receiving, from an entity, a proposed entry for a ledger, where the ledger is shared and accessible by multiple users and includes a whitelist and a blacklist, determining, or assigning, a credibility score and rate limiter value for the entity, comparing the credibility score and rate limiter value with respective credibility score and rate limiter value thresholds, determining that the credibility score and rate limiter value meet or exceed the respective credibility score and rate limiter value thresholds, and submitting the proposed entry to the ledger.

Abstract: One example method includes receiving a group of files, two or more of the files being of different respective file types, creating a backup saveset that includes the group of files, classifying each of the files in the backup saveset based in part on the respective file types of the files, assigning a respective storage media type to each of the classified files in the backup saveset, and transmitting the backup saveset to a storage site.

Abstract: One example method includes receiving an input that indicates selection of a cloud storage provider, receiving one or more product selection inputs, each of the product selection inputs indicating selection of a respective data protection product, receiving an input indicating an instance size, assembling the inputs together to define a data protection configuration, and automatically generating a script which, when executed by one or more hardware processors, deploys the data protection configuration in a cloud storage environment of the selected cloud storage provider.

Abstract: Embodiments for a method of implementing dynamic subgraphs in a graph database by defining vertices of a subgraph descriptively in such a way that the original intent of the content is preserved while the subgraph itself is changing. A database process preserves a “view” of the database as a stored query which will define a subgraph members while the subgraph members are modified, and as opposed to a method in which edges are induced from their vertices to be included in the subgraph, by defining the content of a subgraph descriptively using a database view, vertices which are new and should be contained in the subgraph will automatically be included.

Abstract: Combining a Merkle tree with a graph database by defining a Merkle tree with each node having a hashed value of the metadata for the node and any children of that node, associating non-hashed data with the hashed data for each node, wherein the non-hashed data has an up-pointer from a child node to any of its immediate parent node, and defining bi-directional edges between the nodes of the Merkle tree having a graph database structure to create a reference for the up-pointers associated with each Merkle tree node. The bi-directional edges with up-pointers along with the Merkle tree hash scheme allows efficient traversal of the tree where the hash values indicate non-changed nodes to prevent traversing the database, and further allows efficient path definitions by allowing database processes to walk up edges.