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.