Master data profiling
In an example embodiment, a specialized in-memory database abstraction component is introduced in a cloud cluster. The in-memory database abstraction component may receive lifecycle commands from a client-facing application and interface with a container service to create an in-memory database resource. When parameters are received by the in-memory database abstraction component from the client-facing application, the in-memory database abstraction component may act to validate the parameters, determine if a service plan is available, and determine whether the parameters meet the service plan requirements. If the service plan requirements are not met, the in-memory database abstraction component translates the parameters for the in-memory database resource.
1. A system comprising:
at least one hardware processor; and
a computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
receiving, via a graphical user interface, a selection of a data model, the data model uniquely corresponding to a domain of an enterprise resource processing (ERP) system;
retrieving one or more master data records corresponding to the selected data model;
extracting, from the one or more master data records, model information, the model information including one or more tables, the tables each having one or more fields, each field having a data type and a description, at least one of the fields in one of the tables being dependent upon a field in another of the tables;
storing the model information in an in-memory database;
using an in-memory database management system of the in-memory database, generating, from the model information, a model field table, the model field table containing a first column corresponding to table identifications, a second column corresponding to field identifications, and a third column corresponding to field types;
performing a function on the model field table in response to receiving, via the graphical user interface, a selection of a request to perform the function on the model field table; and
graphically displaying results of the function in the graphical user interface.
2. The system of claim 1 , wherein the model field table further includes a fourth column corresponding to data type in an Advanced Business Application Programming (ABAP) dictionary.
3. The system of claim 1 , wherein the function is to calculate a distribution, and the performing a function include traversing the model field table and, for each field identification in the model field table, identifying a field value that exists in corresponding data in the in-memory database and a count for each time the field value appears in the data.
4. The system of claim 1 , wherein the function is to calculate a contingency table and the performing a function includes, for one or more field identifications, identifying one or more dependent field identifications.
5. The system of claim 4 , further comprising calculating a dependency/correlation level using a ChiSquare algorithm.
6. The system of claim 4 , further comprising calculating a dependency/correlation level using a Cramers V algorithm.
7. The system of claim 1 , wherein the graphically displaying includes displaying a heat map of field value combinations.
8. A method comprising:
receiving, via a graphical user interface, a selection of a data model, the data model uniquely corresponding to a domain of an enterprise resource processing (ERP) system;
retrieving one or more master data records corresponding to the selected data model;
extracting, from the one or more master data records, model information, the model information including one or more tables, the tables each having one or more fields, each field having a data type and a description, at least one of the fields in one of the tables being dependent upon a field in another of the tables;
storing the model information in an in-memory database;
using an in-memory database management system of the in-memory database, generating, from the model information, a model field table, the model field table containing a first column corresponding to table identifications, a second column corresponding to field identifications, and a third column corresponding to field types;
performing a function on the model field table in response to receiving, via the graphical user interface, a selection of a request to perform the function on the model field table; and
graphically displaying results of the function in the graphical user interface.
9. The method of claim 8 , wherein the model field table further includes a fourth column corresponding to data type in an Advanced Business Application Programming (ABAP) dictionary.
10. The method of claim 8 , wherein the function is to calculate a distribution, and the performing a function include traversing the model field table and, for each field identification in the model field table, identifying a field value that exists in corresponding data in the in-memory database and a count for each time the field value appears in the data.
11. The method of claim 8 , wherein the function is to calculate a contingency table and the performing a function includes, for one or more field identifications, identifying one or more dependent field identifications.
12. The method of claim 11 , further comprising calculating a dependency/correlation level using a ChiSquare algorithm.
13. The method of claim 11 , further comprising calculating a dependency/correlation level using a Cramers V algorithm.
14. The method of claim 8 , wherein the graphically displaying includes displaying a heat map of field value combinations.
15. A non-transitory machine-readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving, via a graphical user interface, a selection of a data model, the data model uniquely corresponding to a domain of an enterprise resource processing (ERP) system;
retrieving one or more master data records corresponding to the selected data model;
extracting, from the one or more master data records, model information, the model information including one or more tables, the tables each having one or more fields, each field having a data type and a description, at least one of the fields in one of the tables being dependent upon a field in another of the tables;
storing the model information in an in-memory database;
using an in-memory database management system of the in-memory database, generating, from the model information, a model field table, the model field table containing a first column corresponding to table identifications, a second column corresponding to field identifications, and a third column corresponding to field types;
performing a function on the model field table in response to receiving, via the graphical user interface, a selection of a request to perform the function on the model field table; and
graphically displaying results of the function in the graphical user interface.
16. The non-transitory machine-readable medium of claim 15 , wherein the model field table further includes a fourth column corresponding to data type in an Advanced Business Application Programming (ABAP) dictionary.
17. The non-transitory machine-readable medium of claim 15 , wherein the function is to calculate a distribution, and the performing a function include traversing the model field table and, for each field identification in the model field table, identifying a field value that exists in corresponding data in the in-memory database and a count for each time the field value appears in the data.
18. The non-transitory machine-readable medium of claim 15 , wherein the function is to calculate a contingency table and the performing a function includes, for one or more field identifications, identifying one or more dependent field identifications.
19. The non-transitory machine-readable medium of claim 18 , further comprising calculating a dependency/correlation level using a ChiSquare algorithm.
20. The non-transitory machine-readable medium of claim 18 , further comprising calculating a dependency/correlation level using a Cramers V algorithm.