Abstract: Systems and methods for a flow-based data processing can begin with receiving a selection of a plurality of data processor blocks and a configuration mapping corresponding to the inputs and outputs of each data processor block. At a first compute node associated with a selected data source, an automated data flow can be initiated, wherein the automated data flow is generated from the configuration mapping and comprises a continuous sequence of one or more of the selected plurality of data processor blocks. Data from the selected data source is ingested into the automated data flow and is transformed by the continuous sequence of data processor blocks. The transformed ingested data is transmitted from the automated data flow to a second compute node associated with a selected data destination, via a data accelerator.

Abstract: The present technology can provide a simple to use interface for receiving a selected machine learning task and one or more file pointers indicating a network location where data to be input in the machine learning task is stored. The present technology can also provide a connector that can ingest the input data from the network location; and automatically label the input data to be suitable for the selected machine learning task. The connector can further generate a machine learning compute request comprising a control information specifying one or more parameters for the selected machine learning task and a machine learning dataset generated from the labeled sequences of input data.

Abstract: Disclosed are systems and methods of synchronization between a source and a target. The synchronization relationship can be quickly and easily be created for disaster recovery, real-time backup and failover, thereby ensuring that data on the source is fully-protected at an off-site location or on another server or VM, for example, at another data center, a different building or elsewhere in the cloud. Common snapshots available on both the source and target can act as common recovery points. The common recovery points can be used to locate the most recent snapshot in common, between the source and target, to enable a delta sync of all subsequently written data at the source to the target after an offline event.

Abstract: Disclosed are systems and methods of synchronization between a source and a target. The synchronization relationship can be quickly and easily be created for disaster recovery, real-time backup and failover, thereby ensuring that data on the source is fully-protected at an off-site location or on another server or VM, for example, at another data center, a different building or elsewhere in the cloud. Common snapshots available on both the source and target can act as common recovery points. The common recovery points can be used to locate the most recent snapshot in common, between the source and target, to enable a delta sync of all subsequently written data at the source to the target after an offline event.

Abstract: The present technology can provide a simple to use interface for receiving a selected machine learning task and one or more file pointers indicating a network location where data to be input in the machine learning task is stored. The present technology can also provide a connector that can ingest the input data from the network location; and automatically label the input data to be suitable for the selected machine learning task. The connector can further generate a machine learning compute request comprising a control information specifying one or more parameters for the selected machine learning task and a machine learning dataset generated from the labeled sequences of input data.

Abstract: Systems and methods for a flow-based data processing can begin with receiving a selection of a plurality of data processor blocks and a configuration mapping corresponding to the inputs and outputs of each data processor block. At a first compute node associated with a selected data source, an automated data flow can be initiated, wherein the automated data flow is generated from the configuration mapping and comprises a continuous sequence of one or more of the selected plurality of data processor blocks. Data from the selected data source is ingested into the automated data flow and is transformed by the continuous sequence of data processor blocks. The transformed ingested data is transmitted from the automated data flow to a second compute node associated with a selected data destination, via a data accelerator.

Abstract: Systems and methods for a flow-based data processing can begin with receiving a selection of a plurality of data processor blocks and a configuration mapping corresponding to the inputs and outputs of each data processor block. At a first compute node associated with a selected data source, an automated data flow can be initiated, wherein the automated data flow is generated from the configuration mapping and comprises a continuous sequence of one or more of the selected plurality of data processor blocks. Data from the selected data source is ingested into the automated data flow and is transformed by the continuous sequence of data processor blocks. The transformed ingested data is transmitted from the automated data flow to a second compute node associated with a selected data destination, via a data accelerator.