System and method for creating relational and non-relational databases from structured and unstructured data sources

1 . A computer-implemented method for managing a project workflow over a computer network, the method comprising:

retrieving, by execution of a first application of a computing device and from a second computing device executing a data certification application, a plurality of schemas for handling a workflow process associated with the data certification application, wherein the first application is integrated with the data certification application;

creating, by execution of the first application and based on one of the schemas, a work schedule for a project plan for the data certification application in response to a received request, the project plan comprising a plurality of tasks to be conducted in the data certification application, wherein the data certification application comprises modules for certifying a data quality assessment of a correctness of a plurality of values of a data set;

assigning, by execution of the first application of the computing device, a plurality of resources to each of the tasks in accordance with the project plan and work schedule, the plurality of resources including one or more computing nodes;

restricting, by execution of the first application of the computing device, access privileges in each of the one or more computing nodes based on an active work in progress;

generating, by execution of the first application of the computing device, a graphic user interface based on the access privileges in the one or more computing nodes;

monitoring, by execution of the first application of the computing device, activity of each of the computing nodes progressing through a respective one of the plurality of tasks in the data certification application;

sending, by execution of the first application of the computing device, the monitored activity to a communication device to be rendered in a graphic user interface by the communication device;

receiving, by a machine learning platform executing on the computing device, a value of the data set;

identifying, by the machine learning platform, a pattern associated with the value;

compressing, by the machine learning platform, the pattern;

comparing, by the machine learning platform, the compressed pattern to one or more data type compressed patterns;

validating, by the machine learning platform, a constraint based on a result of the comparison of the compressed pattern to the one or more data type compressed patterns; and

outputting, by the machine learning platform, the value in response to the constraint being validated.

2 . The computer-implemented method in claim 1 , further comprising:

pulling, by the machine learning platform, data type frequencies from an attribute content frequencies database.

3 . The computer-implemented method in claim 2 , further comprising:

sorting, by the machine learning platform, the pulled data type frequencies into descending order, from a highest frequency pulled data type to a lowest frequency pulled data type.

4 . The computer-implemented method in claim 3 , further comprising:

set, by the machine learning platform, the highest frequency pulled data type as a primary data class type.

5 . The computer-implemented method in claim 4 , further comprising:

setting, by the machine learning platform, a next highest frequency pulled data type as a secondary data class type.

6 . The computer-implemented method in claim 5 , further comprising:

comparing, by the machine learning platform, the primary data class type and the secondary data class type to a pulled primary data class type and a pulled secondary data class type.

7 . The computer-implemented method in claim 6 , wherein the computing device is further configured to:

determine, by the machine learning platform, a match for the primary data class type and the secondary data class type.

8 . The computer-implemented method in claim 1 , wherein the computing device is further configured to:

identify, by the machine learning platform, a failed value pattern.

9 . The computer-implemented method in claim 8 , wherein the computing device is further configured to:

compress, by the machine learning platform, the failed value pattern; and

compare, by the machine learning platform, the compressed failed value pattern to a pulled primary identify type valid compressed pattern.

10 . The computer-implemented method in claim 9 , wherein the computing device is further configured to:

extract, by the machine learning platform, a matched pattern value;

validate, by the machine learning platform, the matched pattern value against a data type constraint; and

output, by the machine learning platform, a validated recommended value.

11 . The computer-implemented method in claim 10 , wherein the computing device is further configured to:

send the validated recommended value to the communication device to be rendered in the graphic user interface by the communication device.

12 . The computer-implemented method of claim 1 , further comprising assigning, to each computing node, at least one of a plurality of roles.

13 . The computer-implemented method of claim 12 , wherein the plurality of roles comprises at least one of a project manager, a modeler, a lead, an analyst, or a certifier.

14 . The computer-implemented method of claim 1 , wherein monitoring the activity of each of the computing nodes comprises tracking an amount of time spent progressing through a respective one of the plurality of tasks in the data certification application.

15 . The computer-implemented method of claim 1 , wherein monitoring the activity of each of the computing nodes comprises tracking an amount of time spent by a user of the computing node on the respective one of the plurality of tasks.

16 . The computer-implemented method of claim 1 , further comprising specifying an order in which each of the plurality of tasks is to be conducted.

17 . The computer-implemented method of claim 1 , wherein monitoring the activity of each of the computing nodes comprises an amount of time spent by the computing node in reviewing a record for data certification of the data set.

18 . The computer-implemented method of claim 17 , further comprising reporting the activity of each of the computing nodes progressing through each respective task.

19 . A computer-implemented method for managing a project workflow over a computer network, the method comprising:

retrieving, by execution of a first application of a computing device and from a second computing device executing a second application, a plurality of schemas for handling a workflow process associated with the second application, wherein the first application is integrated with the second application;

creating, by execution of the first application and based on one of the schemas, a work schedule for a project plan for the second application in response to a received request, the project plan comprising a plurality of tasks to be conducted in the second application;

assigning, by execution of the first application of the computing device, a plurality of resources to each of the tasks in accordance with the project plan and work schedule, the plurality of resources including one or more computing nodes;

restricting, by execution of the first application of the computing device, access privileges in each of the one or more computing nodes based on an active work in progress;

generating, by execution of the first application of the computing device, a graphic user interface based on the access privileges in the one or more computing nodes;

monitoring, by execution of the first application of the computing device, activity of each of the computing nodes progressing through a respective one of the plurality of tasks in the second application;

sending, by execution of the first application of the computing device, the monitored activity to a communication device to be rendered in a graphic user interface by the communication device;

receiving, by a machine learning platform executing on the computing device, a value of the data set;

identifying, by the machine learning platform, a pattern associated with the value;

compressing, by the machine learning platform, the pattern;

comparing, by the machine learning platform, the compressed pattern to one or more data type compressed patterns;

validating, by the machine learning platform, a constraint based on a result of the comparison of the compressed pattern to the one or more data type compressed patterns; and

outputting, by the machine learning platform, the value in response to the constraint being validated.

20 . A computer-implemented method for managing a project workflow over a computer network, the method comprising:

creating, by execution of a first application of a computing device, a work schedule for a project plan for a second application in response to a received request, the project plan comprising a plurality of tasks to be conducted on a data set in the second application;

assigning, by execution of the first application of the computing device, a plurality of resources to each of the tasks in accordance with the project plan and work schedule, the plurality of resources including one or more computing nodes;

monitoring, by execution of the first application of the computing device, activity of each of the computing nodes progressing through a respective one of the plurality of tasks in the second application;

sending, by execution of the first application of the computing device, the monitored activity to a communication device to be rendered in a graphic user interface by the communication device;

receiving, by a machine learning platform executing on the computing device, a value of the data set;

identifying, by the machine learning platform, a pattern associated with the value;

compressing, by the machine learning platform, the pattern;

comparing, by the machine learning platform, the compressed pattern to one or more data type compressed patterns;

validating, by the machine learning platform, a constraint based on a result of the comparison of the compressed pattern to the one or more data type compressed patterns; and

outputting, by the machine learning platform, the value in response to the constraint being validated.