Interpreter for interpreting a data model algorithm and creating a data schema
A computing device for interpreting a data model algorithm includes an object searcher, an interpreter, and a translator. The object searcher is configured to search for attributes within datasets generated from at least one method of an instantiation of the data model algorithm in a development mode workflow. The interpreter is configured to evaluate the attributes, identify attributes having a use type, identify the type information of the identified attribute, and create data schema using the identified attributes and type information. The use type can be determined based on attribute values or an interface type associated with an identified attribute. The translator is configured to compare the data schema with another data schema in response to selecting the data model algorithm for inclusion in a production mode workflow.
1. A computing device for interpreting a data model algorithm, the computing device comprising:
an object searcher configured by a processor to search for attributes within at least one dataset generated from at least one method of an instantiation of the data model algorithm in a development mode workflow, wherein the data model algorithm develops data models for a clustered computing environment;
an interpreter configured by a processor to evaluate the attributes, identify attributes having a use type, identify type information of the identified attribute, and create data schema using the identified attributes and type information; and
a translator configured by a processor to:
retrieve production level data schema from the clustered computing environment in response to selecting the data model algorithm for inclusion in a production mode workflow;
translate the data model algorithm from a development state to a production ready state by validating the data schema by comparing the data schema with the production level data schema
for at least one node in the clustered computing environment.
2. The computing device of claim 1 wherein the interpreter is configured to determine the use type by evaluating attribute values associated with an identified attribute.
3. The computing device of claim 1 wherein the interpreter is configured to determine the use type by evaluating an interface type associated with an identified attribute.
4. The computing device of claim 1 wherein each attribute and type define a column name and data type in the data schema.
5. The computing device of claim 1 further comprises the translator configured to compare column heading types of the data schema and the production level data schema to determine validity of the data schema.
6. The computing device of claim 1 wherein the data model algorithm is a Python™ Notebook.
7. A method for interpreting a data model algorithm, the method comprising:
searching for attributes within at least one dataset generated from at least one method of an instantiation of the data model algorithm in a development mode workflow, wherein the data model algorithm develops data models for a clustered computing environment;
evaluating the attributes, identifying attributes having a use type, identifying the type information of the identified attribute, and creating data schema using the identified attributes and type information;
retrieving production level data schema from the clustered computing environment in response to selecting the data model algorithm for inclusion in a production mode workflow; and
translating the data model algorithm from a development state to a production ready state by validating the data schema by comparing the data schema with the production level data schema for at least one node in the clustered computing environment.
8. The method of claim 7 further comprises determining the use type by evaluating attribute values associated with an identified attribute.
9. The method of claim 7 further comprises determining the use type by evaluating an interface type associated with an identified attribute.
10. The method of claim 7 wherein each attribute and type define a column name and data type in the data schema.
11. The method of claim 7 further comprises comparing column heading types of the data schema and the production level data schema to determine validity of the data schema.
12. A non-transitory computer readable storage medium comprising a set of computer instructions executable by a processor for interpreting a data model algorithm, the computer instructions configured to:
search for attributes within at least one dataset generated from at least one method of an instantiation of the data model algorithm in a development mode workflow, wherein the data model algorithm develops data models for a clustered computing environment;
evaluate the attributes, identify attributes having a use type, identify type information of the identified attribute, and create data schema using the identified attributes and type information;
retrieve production level data schema from the clustered computing environment in response to selecting the data model algorithm for inclusion in a production mode workflow; and
translate the data model algorithm from a development state to a production ready state by validating the data schema by comparing the data schema with the production level data schema for at least one node in the clustered computing environment.
13. The non-transitory computer readable storage medium of claim 12 further comprises computer instructions configured to determine the use type by evaluating attribute values associated with an identified attribute.
14. The non-transitory computer readable storage medium of claim 12 further comprises computer instructions configured to determine the use type by evaluating an interface type associated with an identified attribute.
15. The non-transitory computer readable storage medium of claim 12 wherein each attribute and type define a column name and data type in the data schema.
16. The non-transitory computer readable storage medium of claim 12 further comprises computer instructions configured to compare column heading types of the data schema and the production level data schema to determine validity of the data schema.
17. The non-transitory computer readable storage medium of claim 12 wherein the data model algorithm is a Python™ Notebook.