Python Testify vs NBi comparison of testing frameworks
What are the differences between Python Testify and NBi?

	Python Testify https://github.com/Yelp/Testify	NBi http://www.nbi.io/
Programming language	Python	.NET
Category	Unit Testing	Integration Testing, Unit Testing, Acceptance Testing
General info	A Python unit testing framework modelled after unittest Testify is modelled after unittest but has more features while still supporting unittest classes. It has more pythonic naming conventions, an better test runner output visually, a decorator-based approach to fixture methods among many other features	NBi is an open-source framework for testing Business Intelligence solutions or validating data quality. NBi helps you to create tests targeting your databases, cubes, etls and reports. Tests are written in xml using an intuitive syntax therefore thereis no need of any development language. Nbi tests target databases, cubes, etls and reports
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	No
Client-side Allows testing code execution on the client, such as a web browser	Yes Front-end functionality and behaviour can be tested by Testify.	No
Server-side Allows testing the bahovior of a server-side code	Yes Testify can test various server and database behaviours and functionality	Yes Nbi tests Business intelligence software which retrieve, analyze, transform and report data therefore it targets databases, cubes, etls and reports and you can natively connect to any database supporting OleDb or ODBC connection
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	Yes Fixture methods are supported and it follows a decorator based approach, that is they are written similar to decorators	No
Group fixtures Allows defining a fixed, specific states of data for a group of tests (group-fixtures). This ensures specific environment for a given group of tests.	Yes Group fixtures are supported	No
Generators Supports data generators for tests. Data generators generate input data for test. The test is then run for each input data produced in this way.	Yes One can create generator methods to yield runnable test methods which will pick out the test methods from your TestCases, and then exclude any in any of your exclude_suites method.If there are any require_suites, it will then further limit itself to test methods in those suites.	N/A
Licence Licence type governing the use and redistribution of the software	Apache License 2.0	Apache License 2.0
Mocks Mocks are objects that simulate the behavior of real objects. Using mocks allows testing some part of the code in isolation (with other parts mocked when needed)	Yes It includes the turtle mock object library	Yes You can create your own mock objects
Grouping Allows organizing tests in groups	Yes Testify includes support for detecting and running test suites, grouped by modules, classes, or individual test methods.	Yes Yes, Nbi comes with a solution to automate, as much as possible, the creation of the test-suites through its user interface, named GenBI
Other Other useful information about the testing framework