Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for implementing data requests associated with objects of an object store. A data connector component may be instantiated as a container for processing data requests associated with backup data stored within objects of an object store. The data connector component may evaluate the object store to identify snapshots stored as the backup data within the objects of the object store according to an object format. The data connector component may provide a client device with access to backup data of the snapshots.

Abstract: Techniques are provided for a snapshot difference interface integrated into an object store data management container. The snapshot difference interface is capable of interpreting an object format and snapshot file system format of snapshots backed up to an object store within objects formatted according to the object format. The snapshot difference interface can identify differences between snapshots, such as files that changed between the snapshots, while the snapshots are still resident within the object store. Because the snapshot difference interface does not retrieve the snapshots from the object store, security is improved, resource and network consumption is reduced, there is less of an impact upon client I/O processing, and a catalog of the snapshots can be more efficiently built and recovered in the event of corruption.

Abstract: Techniques are provided for a snapshot difference interface integrated into an object store data management container. The snapshot difference interface is capable of interpreting an object format and snapshot file system format of snapshots backed up to an object store within objects formatted according to the object format. The snapshot difference interface can identify differences between snapshots, such as files that changed between the snapshots, while the snapshots are still resident within the object store. Because the snapshot difference interface does not retrieve the snapshots from the object store, security is improved, resource and network consumption is reduced, and there is less of an impact upon client I/O processing. Also, a compliance scan for the snapshots can be performed much quicker by skipping already scanned snapshot data from a prior compliance scan.

Abstract: Techniques are provided for determining a physical size of a snapshot backed up to an object store. Snapshot data of the snapshot may be backed up into objects that are stored from a node to the object store, such as a cloud computing environment. A tracking object is created to identify which objects within the object store comprise the snapshot data of the snapshot. In order to determine the physical size of the snapshot, the tracking object and/or tracking objects of other snapshots such as a prior snapshot are evaluated to identify a set of objects comprising snapshot data unique to the snapshot and not shared with the prior snapshot. The physical sizes of the set of objects are combined with a metadata size of metadata of the snapshot to determine the physical size of the snapshot.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for implementing a snapshot copy operation between endpoints. One or more snapshots (e.g., snapshots of an on-premise volume) is stored within a source endpoint, such as a source bucket of an object store. A post operation is executed to copy objects comprising snapshot data of a snapshot from the source endpoint to a destination endpoint. A get operation and a tracking object such as a cookie is used to track progress of copying the objects from the source endpoint to the destination endpoint. The tracking object is used to restart the copying of the objects from a point where the copying left off (e.g., in the event there is a failure) without having to restart from the beginning.

Abstract: Techniques are provided for implementing data requests associated with objects of an object store. A data connector component may be instantiated as a container for processing data requests associated with backup data stored within objects of an object store. The data connector component may evaluate the object store to identify snapshots stored as the backup data within the objects of the object store according to an object format. The data connector component may provide a client device with access to backup data of the snapshots.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for implementing data requests associated with objects of an object store. A data connector component may be instantiated as a container for processing data requests associated with backup data stored within objects of an object store. The data connector component may evaluate the object store to identify snapshots stored as the backup data within the objects of the object store according to an object format. The data connector component may provide a client device with access to backup data of the snapshots.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for implementing management requests associated with objects of an object store. A data connector component may be instantiated as a container for processing management requests associated with backup data stored within an object store as an object according to an object format. A management request associated with the backup data may be received by the data connector component. A structure associated with the object having the object format may be traversed by the data connector component to identify the backup data. The data connector component may be implemented upon the backup data stored within the object.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.

Abstract: Techniques are provided for on-demand creation and/or utilization of containers and/or serverless threads for hosting data connector components. The data connector components can be used to perform integrity checking, anomaly detection, and file system metadata analysis associated with objects stored within an object store. The data connector components may be configured to execute machine learning functionality to perform operations and tasks. The data connector components can perform full scans or incremental scans. The data connector components may be stateless, and thus may be offlined, upgraded, onlined, and/or have tasks transferred between data connector components. Results of operations performed by the data connector components upon base objects may be stored within sibling objects.