Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: A cross-platform data migration method and a cross-platform data migration system include generating backup data of a virtual machine in a host system, identifying a first file format associated with backup data, converting the first file format associated with backup data into a platform-neutral file format associated with backup data, and storing the backup data in the platform-neutral file format in a first storage platform.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: Described herein is a system that processes personal data in databases. The system samples data stored in columns of data tables and analyzes the sampled data to determine whether the sampled data includes personal data. Based on the analysis, the system marks which data tables and which columns of the data tables store personal data. The system receives a request to process personal data for a subject. From data tables that are marked as storing personal data, the system identifies records storing personal data for the subject. The system additionally identifies other data tables marked as storing personal data that reference or are referenced by the data tables including the records referencing the subject. The system processes the data stored in the columns that are marked as storing personal data.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data of an application distributed across a set of machines of a compute infrastructure. A DMS node associates a set of machines with the application, and generates data fetch jobs for the set of machines for execution by multiple peer DMS nodes. The DMS node determining whether each of the data fetch jobs for the set of machines is ready for execution by the peer DMS nodes. In response to determining that each of the data fetch jobs is ready for execution, the peer DMS nodes execute the data fetch jobs to generate snapshots of the set of machines. The snapshots may be full or incremental snapshots, and collectively form a snapshot of the application.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: Methods and systems for reducing the amount of time to restore a database or other application by dynamically generating and storing synthetic snapshots are described. When backing up a database, an integrated data management and storage system may acquire snapshots of the database at a snapshot frequency and acquire database transaction logs at a frequency that is greater than the snapshot frequency. In response to detecting that the database is unable to provide a database snapshot, the integrated data management and storage system may generate a synthetic snapshot of the database by instantiating a compatible version of the database locally, acquiring a previously stored snapshot of the database, applying data changes from one or more database transaction logs to the previously stored snapshot to generate the synthetic snapshot, and storing the synthetic snapshot of the database within the integrated data management and storage system.

Abstract: Methods and systems for reducing the amount of time to restore a database or other application by dynamically generating and storing synthetic snapshots are described. When backing up a database, an integrated data management and storage system may acquire snapshots of the database at a snapshot frequency and acquire database transaction logs at a frequency that is greater than the snapshot frequency. In response to detecting that the database is unable to provide a database snapshot, the integrated data management and storage system may generate a synthetic snapshot of the database by instantiating a compatible version of the database locally, acquiring a previously stored snapshot of the database, applying data changes from one or more database transaction logs to the previously stored snapshot to generate the synthetic snapshot, and storing the synthetic snapshot of the database within the integrated data management and storage system.

Abstract: An example method of managing and storing data includes pulling and saving a first snapshot of a first state of a target machine running on a cloud service and creating a first virtual machine (VM) package associated with the first snapshot. The first VM package sufficient to instantiate a VM emulating the target machine with the first state on an on-premise destination VM platform. The method includes pulling and saving a second snapshot of a second state of the target machine, wherein the second state occurs after the first state. A second VM package is created and associated with the second snapshot, the second VM package sufficient to instantiate the VM emulating the target machine with the second state on the destination VM platform. The second VM package is created by updating the first VM package according to differences between the first snapshot and the second snapshot.

Abstract: A cross-platform data migration method and a cross-platform data migration system include generating backup data of a virtual machine in a host system, identifying a first file format associated with backup data, converting the first file format associated with backup data into a platform-neutral file format associated with backup data, and storing the backup data in the platform-neutral file format in a first storage platform.

Abstract: Described herein is a system that processes personal data in databases. The system samples data stored in columns of data tables and analyzes the sampled data to determine whether the sampled data includes personal data. Based on the analysis, the system marks which data tables and which columns of the data tables store personal data. The system receives a request to process personal data for a subject. From data tables that are marked as storing personal data, the system identifies records storing personal data for the subject. The system additionally identifies other data tables marked as storing personal data that reference or are referenced by the data tables including the records referencing the subject. The system processes the data stored in the columns that are marked as storing personal data.

Abstract: Described herein is a system that processes personal data in databases. The system samples data stored in columns of data tables and analyzes the sampled data to determine whether the sampled data includes personal data. Based on the analysis, the system marks which data tables and which columns of the data tables store personal data. The system receives a request to process personal data for a subject. From data tables that are marked as storing personal data, the system identifies records storing personal data for the subject. The system additionally identifies other data tables marked as storing personal data that reference or are referenced by the data tables including the records referencing the subject. The system processes the data stored in the columns that are marked as storing personal data.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data backup and disaster recovery using a service level agreement. The backup data can be replicated from a primary compute infrastructure to the secondary compute infrastructure. For example, primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. If the primary compute infrastructure is lost, the secondary compute infrastructure is initialized to replicate the primary compute infrastructure. Applications that were running in the primary compute infrastructure can failover to run in the secondary compute infrastructure. The replicated backup data is used to facilitate the migration. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines.

Abstract: The disclosed technology teaches finding contents of one or more virtual machines running on one or more cloud servers. The disclosed technology includes a cloud snapshot metadata manager periodically instantiating indexing virtual machines on the cloud servers. The indexing virtual machines compile metadata of one or more virtual machines on the cloud server from one or more snapshot file systems of the virtual machines. The indexing virtual machines then transmit the compiled metadata to the cloud snapshot metadata manager. The cloud snapshot metadata manager forwards the metadata to a client and causing the client to create an index of the compiled metadata from multiple virtual machine snapshots on multiple clouds and responding to queries from a user using the index of compiled metadata, without requiring the user to instantiate or to attach to the snapshots.

Abstract: Methods and systems for reducing the amount of time to restore a database or other application by dynamically generating and storing synthetic snapshots are described. When backing up a database, an integrated data management and storage system may acquire snapshots of the database at a snapshot frequency and acquire database transaction logs at a frequency that is greater than the snapshot frequency. In response to detecting that the database is unable to provide a database snapshot, the integrated data management and storage system may generate a synthetic snapshot of the database by instantiating a compatible version of the database locally, acquiring a previously stored snapshot of the database, applying data changes from one or more database transaction logs to the previously stored snapshot to generate the synthetic snapshot, and storing the synthetic snapshot of the database within the integrated data management and storage system.

Abstract: Methods and systems for efficiently capturing snapshots of a computing application or environment over time and transferring the snapshots to an integrated data management and storage system are described. A snapshot agent may detect that one or more electronic files associated with the computing application or environment are greater than a threshold file size and in response perform an incremental backup optimization in which the snapshot agent may identify files that have been touched since a previous snapshot by accessing file system metadata (e.g., last modified timestamps) or utilizing a tracking agent to detect potential file changes that have occurred since the previous snapshot was captured. The snapshot agent may then generate fingerprints for data blocks of the touched files, which may reduce the total number of fingerprints needing to be generated to identify the changed data blocks corresponding with a current snapshot to be transferred.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages data backup and disaster recovery using a service level agreement. The backup data can be replicated from a primary compute infrastructure to the secondary compute infrastructure. For example, primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. If the primary compute infrastructure is lost, the secondary compute infrastructure is initialized to replicate the primary compute infrastructure. Applications that were running in the primary compute infrastructure can failover to run in the secondary compute infrastructure. The replicated backup data is used to facilitate the migration. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines.

Abstract: A data management and storage (DMS) cluster of peer DMS nodes manages migration of an application between a primary compute infrastructure and a secondary compute infrastructure. The secondary compute infrastructure may be a failover environment for the primary compute infrastructure. Primary snapshots of virtual machines of the application in the primary compute infrastructure are generated, and provided to the secondary compute infrastructure. During a failover, the primary snapshots are deployed in the secondary compute infrastructure as virtual machines. Secondary snapshots of the virtual machines are generated, where the secondary snapshots are incremental snapshots of the primary snapshots. In failback, the secondary snapshots are provided to the primary compute infrastructure, where they are combined with the primary snapshots into construct a current state of the application, and the application is deployed in the current state by deploying virtual machines on the primary compute infrastructure.

Abstract: An example method of managing and storing data includes pulling and saving a first snapshot of a first state of a target machine running on a cloud service and creating a first virtual machine (VM) package associated with the first snapshot. The first VM package sufficient to instantiate a VM emulating the target machine with the first state on an on-premise destination VM platform. The method includes pulling and saving a second snapshot of a second state of the target machine, wherein the second state occurs after the first state. A second VM package is created and associated with the second snapshot, the second VM package sufficient to instantiate the VM emulating the target machine with the second state on the destination VM platform. The second VM package is created by updating the first VM package according to differences between the first snapshot and the second snapshot.