Abstract: In some examples, performing a backup of a group of relational databases comprises identifying the group of relational databases; identifying I/O traffic flowing into or out of the group of relational databases; temporarily freezing the I/O traffic flowing into or out of the group of relational databases; identifying a set of batch backup operations to be performed during the temporary I/O freeze; identifying relational dependencies between at least some databases in the group of relational databases; based on the identified relational dependencies, identifying, among the set of batch backup operations, a first sub-set of backup operations eligible to be performed in parallel, and a second sub-set of backup operations not eligible to be performed in parallel; conducting the backup, wherein conducting the backup comprises performing the identified first sub-set of backup operations in parallel and performing the identified second sub-set of backup operations sequentially; and unfreezing the I/O traffic.

Abstract: Methods and systems for improving data back-up, recovery, and search across different cloud-based applications, services, and platforms are described. A data management and storage system may direct compute and storage resources within a customer's cloud-based data storage account to back-up and restore data while the customer retains full control of their data. The data management and storage system may direct the compute and storage resources within the customer's cloud-based data storage account to generate and store secondary layers that are used for generating search indexes, to generate and store shared space layers and user specific layers to facilitate the deduplication of email attachments and text blocks, to perform a controlled restoration of email snapshots such that sensitive information (e.g., restricted keywords) located within stored snapshots remains protected, and to detect and preserve emails that were received or transmitted and then deleted between two consecutive snapshots.

Abstract: In some examples, a method comprises: receiving a request to read data within a specified range from a backup file storing at least one base snapshot and at least one incremental snapshot; looking up the specified range in range filters from the backup file, the range filters corresponding to snapshots stored in the backup file and each range filter comprising bits indicating whether data exists at respective ranges within the snapshot corresponding to the respective range filter; and in response to the looking up, reading the requested data from the looked-up range in the backup file.

Abstract: A data management system (DMS) may select, prior to obtaining a first snapshot of a first virtual machine (VM) and from among one or more snapshots previously obtained by the DMS, a second snapshot to use for deduplication of the first snapshot. The DMS may obtain the first snapshot after selecting the second snapshot. Obtaining the first snapshot may include writing a first subset of data blocks from the first VM to a snapshot file for the first snapshot based on the first subset of the data blocks from the first VM being different from a first corresponding subset of the second snapshot and refraining from writing a second subset of the data blocks from the first VM to the snapshot file for the first snapshot based on the second subset of the data blocks from the first VM matching a second corresponding subset of the second snapshot.

Abstract: In some examples, a data management and storage (DMS) platform, comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and at least one processor configured to perform operations in a method of creating a local consolidated patch file from a patch file chain stored in the cloud storage. The operations include, in a first dry-run phase, creating a logical patch file image of data blocks in one or more cloud patch files stored in the cloud storage; in a second data-transfer phase, downloading at least some of the data blocks from the cloud patch files identified by the logical patch file image, the second data-transfer phase comprising a coalescing operation to construct a set of coalesced reads of the data blocks; and creating and storing, in the local storage, the local consolidated patch file using the downloaded data blocks.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for container based application reification. In a particular embodiment, an application reification system is provided including one or more computer readable storage media and a processing system operatively coupled with the one or more computer readable storage media. The application reification system further includes program instructions stored on the one or more computer readable storage media that, when read and executed by the processing system, direct the processing system to preserve a version of application data at a first time and a configuration of an application at the first time. At a second time subsequent to the first time, the program instructions direct the processing system to create a template for a container containing the application in the configuration and a pointer to the version of the application data in a secondary storage repository.

Abstract: A second data source may retrieve metadata for one or more versions of a set of versions of a file stored at the first data source. In some examples, the metadata for the one or more versions of the file may include at least an identifier of the file, a timestamp, and a cryptographic signature. In some examples, generation of the cryptographic signature may be based on the identifier of the file, the timestamp, and a cryptographic key. The second data source may identify a set of versions of the file that were uploaded from a trusted data source to the first data source based on a comparison of the cryptographic signature to a computed cryptographic signature. The second data source may then determine a targeted version of the file and retrieve the targeted version of the file from the first data source.

Abstract: Techniques for implementing a scalable automated training framework for anomaly and ransomware detection are disclosed. A computer system may instantiate one or more virtual machines (VMs). Each VM may be loaded with a corresponding file system. The computer system may simulate, on the one or more VMs, user actions and ransomware, which may cause changes to the corresponding file systems loaded on to the VMs. The computer system may obtain snapshots of the VMs that indicate changes to the corresponding file system of each of the VMs. The computer system may generate a metadata file for each VM based on the corresponding snapshot. The computer system may generate training data for training a ransomware detection model using a machine learning algorithm based on the metadata files for each of the VMs.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for data lineage based multi-data store recovery. In a particular embodiment, a method provides identifying first data in a first table of a plurality of tables stored in a plurality of data stores and restoring the first data to a first correct version of the first data in a prior version of the first table. The method further provides identifying a second table of the plurality of tables that descends from the first table and includes second descendent data that stems from the first data. The method also provides restoring the second descendent data to a second correct version of the second descendent data in a prior version of the second table.

Abstract: Methods, systems, and devices for data management are described. A data management system (DMS) may determine to generate a backup instance of an application that includes multiple user accounts. The DMS may identify a time window for generation of the backup instance based on a set of backup parameters associated with the application. The DMS may calculate respective runtime durations for the user accounts based on respective quantities of data objects in the user accounts and respective backup job types associated with the data objects. The DMS may determine respective values of a semaphore parameter based on the respective runtime durations and the time window, where the semaphore parameter defines a threshold quantity of data objects to be processed in parallel. The DMS may generate the backup instance of the application based on processing the user accounts in accordance with the respective values of the semaphore parameter.

Abstract: In some examples, an anomaly database system is provided for processing metrics in telemetry data. An example anomaly database system comprises a continuous data management (CDM) node, the CDM node including a metrics library for sending out system metrics in a sparse manner and a statistics relay for receiving streaming metrics from nodes in a node cluster, the node cluster including the CDM node, the statistics relay pushing the received metrics to a metrics collector. A sparse consumers module pulls metrics, from the metrics collector, pushed to the metrics collector by the statistics relay.

Abstract: A data management platform may receive, from a user of a data management platform, a first job request to perform a backup of data from a data source to a database managed by the user. In some examples, the database may be configured as a set of database instances running on a set of computing nodes of a computing cluster. The data management platform may store a backup load indication that indicates which computing node is assigned to perform the backup of the data based on receiving the first job request. The data management platform may receive one or more second job requests subsequent to receiving the first job request and may determine a backup load for one or more computing nodes of the set of computing nodes. The data management platform may then assign one or more target computing nodes for performing the one or more second job requests.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for sub-cluster recovery in a data storage environment having a plurality of storage nodes. In a particular embodiment, the method provides scanning data items in the plurality of nodes. While scanning, the method further provides indexing the data items into an index of a plurality of partition groups. Each partition group includes data items owned by a particular one of the plurality of storage nodes. The method then provides storing the index.

Abstract: A data management system (DMS) may facilitate snapshot exports between a snapshot storage account and a computing system. The DMS may receive, from the computing system, a request to download data associated with one or more snapshots stored in the snapshot storage account in a first cloud environment. The DMS may generate an export job for execution by one or more computing resources within the first cloud environment based on the request. The export job may be operable to decrypt the data and write the decrypted data to an export storage account in a second cloud environment. The DMS may generate an encrypted download link that is operable to download the decrypted data from the export storage account. The DMS may transmit the encrypted download link to the computing system associated with the request to download the data.

Abstract: Methods, systems, and devices for data management are described. A first procedure for capturing first point-in-time data for a data object may be performed and may include initiating a first type of operation to back up the first point-in-time data, the first operation being native to the data object and enabling sections of files to be backed up in parallel on a per-section basis. A second procedure for restoring the first point-in-time data to the computing system may also be performed. Performing the second procedure may include executing, based on recreating the first point-in-time data, the first type of operation to backup sections of first files of the first point-in-time data in parallel on the per-section basis to the computing system. And further include initiating a second type of operation to restore second files of the first point-in-time data in parallel on a per-file basis to the computing system.

Abstract: According to various embodiments, with respect to a target set of files being managed (e.g., protected by data snapshots), each file in the target set of files is classified into one of two or more filesets (discontiguous filesets), where each of these filesets comprises one or more files that are related to each other by one or more factors, such as frequency of file change or purpose of existence (e.g., used by a software application). Once classified, files within the target set of files can be uniquely processed by a data management operation (e.g., incremental data snapshot process) according to their association to a discontiguous fileset.

Abstract: Methods for establishing a second database and maintaining synchronization between a first database and the second database in a data management system are described. According to the method, a snapshot of a state of the first database may be acquired and mounted to a second server. The second database may be restored to the second server based on the mount. The second database may replicate the state of the first database. Synchronization may be enabled between the first database and the second database. One or more metrics associated with replication of data between the databases may be identified. A backup process for transaction logs associated with the first database may be initiated and the transaction logs may be mounted to the second server based on the metrics. One or more transactions may be applied to the second database based on the transaction logs mounted to the second server.

Abstract: A method for updating configuration settings of a backup database supported by a data management system is described. The method may include receiving, from a first user in a first user group, a request to update configuration settings of the backup database. The method may further include determining that the first user is authorized to update the configuration settings of the backup database based on a set of permissions associated with the first user. The method may further include identifying a second user in a second user group that is authorized to approve the request from the first user. The method may further include transmitting an indication of the request to the second user and receiving a notification that the second user has approved the request from the first user. The method may further include updating the configuration settings of the backup database in response to the notification.

Abstract: A data backup and recovery method and system using allocated data blocks include identifying a first snapshot associated with a virtual machine; accessing changed block tracking data associated with data changes occurred in the virtual machine, the data changes corresponding to a set of changed data blocks; accessing block allocation status data associated with the set of changed data blocks; identifying one or more allocated data blocks from the set of changed data blocks that are associated with allocated status based on the block allocation status data; and storing the one or more allocated data blocks to a storage device.

Abstract: Described herein are systems and methods that manage machine backups, including the creation of virtual machine packages sufficient to instantiate virtual machines corresponding to the backups. In one aspect, a compute infrastructure includes many machines, which may be either physical or virtual. From time to time, snapshots of the states of these target machines are pulled and saved. Virtual machine packages corresponding to these snapshots are also created. A virtual machine package can be used to instantiate a virtual machine (VM) emulating the target machine with the saved state on a destination virtual machine platform. At some point, the initial VM package for a target machine is created by converting the snapshot to a VM package. However, this may take a long time. Later VM packages can instead be created by updating a prior VM package according to differences between the corresponding snapshots, rather than performing the full conversion process.