1. A method for dynamically modeling and monitoring performance data in a computing environment, the method comprising: by a computer system comprising computer hardware: receiving performance data collected by at least one agent module from multiple applications executing in multiple servers in a computing environment, wherein the performance data includes a first data tree including a plurality of different types of data nodes, wherein the different types of data nodes are associated with different time series metrics about the multiple servers and the multiple applications executing in the multiple servers; automatically accessing a first model template of a plurality of model templates wherein the model template includes a topology of configuration nodes representing the expected structure of the performance data; automatically comparing properties of each of the plurality of different types of data nodes of the first data tree with properties of the configuration nodes of the first model template to identify matches between at least a portion of the plurality of different types of data nodes and a portion of the configuration nodes; automatically generating a modified model of the first model template that includes the topology of configuration nodes in the first model template that match the structure of the plurality of different types of data nodes in the first data tree, wherein the modified model includes a plurality of interconnected topology objects representing the multiple applications executing in the multiple servers of the computing environment; and storing the modified model in a data repository.
1. A method for dynamically modeling and monitoring performance data in a computing environment, the method comprising: by a computer system comprising computer hardware: receiving performance data collected by at least one agent module from multiple applications executing in multiple servers in a computing environment, wherein the performance data includes a first data tree including a plurality of different types of data nodes, wherein the different types of data nodes are associated with different time series metrics about the multiple servers and the multiple applications executing in the multiple servers; automatically accessing a first model template of a plurality of model templates wherein the model template includes a topology of configuration nodes representing the expected structure of the performance data; automatically comparing properties of each of the plurality of different types of data nodes of the first data tree with properties of the configuration nodes of the first model template to identify matches between at least a portion of the plurality of different types of data nodes and a portion of the configuration nodes; automatically generating a modified model of the first model template that includes the topology of configuration nodes in the first model template that match the structure of the plurality of different types of data nodes in the first data tree, wherein the modified model includes a plurality of interconnected topology objects representing the multiple applications executing in the multiple servers of the computing environment; and storing the modified model in a data repository. 9. The method of claim 1 , additionally comprising modifying a second model based on changes made while generating the modified model.