Abstract: In an embodiment, an apparatus includes one or more processors configured to receive at least one command line interface command, generate a push notification associated with the at least one command line interface command, send the push notification to at least one managed device, responsive to the at least one managed device receiving the push notification, receive a pull request from the at least one managed device, responsive to receiving the pull request, send the at least one command line interface command to a device-specific adaptor of the at least one managed device such that the device-specific adaptor converts the at least one command line interface command to a device-specific command associated with the at least one managed device, and receive an execution status from the at least one managed device in response to the device-specific command being executed by the at least one managed device.

Abstract: In an embodiment, an apparatus includes one or more processors configured to receive at least one command line interface command, generate a push notification associated with the at least one command line interface command, send the push notification to at least one managed device, responsive to the at least one managed device receiving the push notification, receive a pull request from the at least one managed device, responsive to receiving the pull request, send the at least one command line interface command to a device-specific adaptor of the at least one managed device such that the device-specific adaptor converts the at least one command line interface command to a device-specific command associated with the at least one managed device, and receive an execution status from the at least one managed device in response to the device-specific command being executed by the at least one managed device.

Abstract: Techniques are disclosed for attribution analysis in analytical workflows. A data processing system (DPS) obtains an overall model comprising one or more sub-models. The DPS selects an output variable of the overall model for which attribution of changes is to be performed, and a plurality of input variables against which changes are to be attributed to. The overall model is initially executed with respect to a data set of values for the plurality of input variables to generate a base result for the output variable. The overall model is iteratively executed based on a condition associated with the plurality of input variables to obtain a new result for the output variable. In each iteration, a value of an input variable is changed with respect to the data set of values and a change in the output variable with respect to the base result is attributed to the corresponding input variable.

Abstract: Techniques are disclosed for attribution analysis in analytical workflows. A data processing system (DPS) obtains an overall model comprising one or more sub-models. The DPS selects an output variable of the overall model for which attribution of changes is to be performed, and a plurality of input variables against which changes are to be attributed to. The overall model is initially executed with respect to a data set of values for the plurality of input variables to generate a base result for the output variable. The overall model is iteratively executed based on a condition associated with the plurality of input variables to obtain a new result for the output variable. In each iteration, a value of an input variable is changed with respect to the data set of values and a change in the output variable with respect to the base result is attributed to the corresponding input variable.

Abstract: Techniques for retaining in-memory dataframes beyond an in-memory processing session. One technique includes receiving a request to execute a first run having a first set of tasks, creating a first session to execute the first run, and executing the first run in the first session using a dataframe constructed for a dataset defined as a component of the first run. The executing the first run generates an updated dataframe. The technique further includes receiving a request to execute a second run having a second set of tasks. A dependency exists between the first run and the second run based on a condition that the dataset is defined as a component of the first run and the second run. The technique further includes creating a second session to execute the second run, and executing the second run in the second session using the updated dataframe for the dataset.

Abstract: Techniques for retaining in-memory dataframes beyond an in-memory processing session. One technique includes receiving a request to execute a first run having a first set of tasks, creating a first session to execute the first run, and executing the first run in the first session using a dataframe constructed for a dataset defined as a component of the first run. The executing the first run generates an updated dataframe. The technique further includes receiving a request to execute a second run having a second set of tasks. A dependency exists between the first run and the second run based on a condition that the dataset is defined as a component of the first run and the second run. The technique further includes creating a second session to execute the second run, and executing the second run in the second session using the updated dataframe for the dataset.