22. A computer-implemented method comprising: retrieving node data, from a node device, wherein the node data is descriptive of at least one characteristic of an execution environment of the node device; generating a current data set model, at a coordinating device, wherein the current data set model is descriptive of at least one characteristic of a current data set; comparing the current data set model to at least one previously generated data set model of at least one previously analyzed data set to detect a match in the at least one characteristic that meets a similarity threshold, wherein the at least one previously generated data set model is descriptive the of at least one characteristic of the at least one previously analyzed data set; in response to detection of a match that meets the similarity threshold between the at least one characteristic described in the current data set model and the at least one characteristic described in the at least one previously generated data set model: retrieving an indication from a correlation database of at least one previously performed action previously performed on the at least one previously analyzed data set, wherein the at least one previously performed action comprises at least one of normalization, a transform or a task performed on the at least one previously analyzed data set; selecting a computer language based on the node data; generating, at the coordinating device, node instructions, in the selected computer language and based on at least the current data set model, to cause the node device to perform the at least one previously performed action on at least a portion of the current data set; and transmitting the node instructions to the node device; monitoring previously performed actions performed on each previously analyzed data set of the at least one previously analyzed data set; for each previously analyzed data set of the at least one previously analyzed data set, storing previously generated data set model that corresponds to the previously analyzed data set correlated to an indication of a previously performed action performed on the previously analyzed data set in the correlation database; deriving a correlation between the at least one characteristic and the at least one previously performed action from the correlation database; and determining the at least one previously performed action to cause the node device to perform on the current data set based on the correlation.
22. A computer-implemented method comprising: retrieving node data, from a node device, wherein the node data is descriptive of at least one characteristic of an execution environment of the node device; generating a current data set model, at a coordinating device, wherein the current data set model is descriptive of at least one characteristic of a current data set; comparing the current data set model to at least one previously generated data set model of at least one previously analyzed data set to detect a match in the at least one characteristic that meets a similarity threshold, wherein the at least one previously generated data set model is descriptive the of at least one characteristic of the at least one previously analyzed data set; in response to detection of a match that meets the similarity threshold between the at least one characteristic described in the current data set model and the at least one characteristic described in the at least one previously generated data set model: retrieving an indication from a correlation database of at least one previously performed action previously performed on the at least one previously analyzed data set, wherein the at least one previously performed action comprises at least one of normalization, a transform or a task performed on the at least one previously analyzed data set; selecting a computer language based on the node data; generating, at the coordinating device, node instructions, in the selected computer language and based on at least the current data set model, to cause the node device to perform the at least one previously performed action on at least a portion of the current data set; and transmitting the node instructions to the node device; monitoring previously performed actions performed on each previously analyzed data set of the at least one previously analyzed data set; for each previously analyzed data set of the at least one previously analyzed data set, storing previously generated data set model that corresponds to the previously analyzed data set correlated to an indication of a previously performed action performed on the previously analyzed data set in the correlation database; deriving a correlation between the at least one characteristic and the at least one previously performed action from the correlation database; and determining the at least one previously performed action to cause the node device to perform on the current data set based on the correlation. 23. The computer-implemented method of claim 22 , comprising: retrieving a first portion of the current data set from a storage device; identifying at least one characteristic of the first portion to generate first descriptive data descriptive of the at least one characteristic of the portion of the current data set; retrieving, from the node device, second descriptive data that is descriptive of at least one characteristic of a second portion of the current data set stored by the node device; combining at least the first and second descriptive data to generate an annotated metadata, wherein the annotated metadata comprises an indication of at least one of a label by which the current data set is organized, a delimiter used to separate two data items within the current data set, or a format of a data item within the current data set; and generating, at the coordinating device, the current data set model from the annotated metadata, wherein the at least one characteristic of the current data set comprises at least one of a quantity of rows into which the current data set is organized, a quantity of data items that employ a specified format, a degree of sparseness of the current data set, or a degree of cardinality of the current data set.