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Python is like a candy store and it is so easy to deploy a package and start using it. I'll try to explain yet keep in mind that I prefer working with LabVIEW end to end mainly because of maintenance 5 years from now. I don't want to employ a C, python or whatever language programmer forever and ever. Building a well documented and automated tool in one language makes the development somewhat limited but the result should be much more stable in the long run without issues rising from bad communication between departments. Let's say you have several generic test-benches with different devices in them that can test different uuts. There are versioned generic drivers for those test-benches that can be operated via an API. I'll open the browser and communicate to the server back and forth through xml rpc. The server will decide which generic driver to deploy and through celery it will decide which uut is going to be tested where and when and by whom. The server will send the relevant flow of uut test. The db with matchmaking of station+uut+sequence of a test is mongo db since the structures are not as strong as in sql, it fits the development in a much more harmonic way. Take notice that this way a control in a function doesn't have to keep all the inheritance limitations in an OO HAL. You simply deploy the relevant test sequence. Finally, Kibana will create reports from the results collected with recipes which are fast again since the mongodb is tailored to the development and doesn't enforce an sql design which might be slow for a future query that the sql design was not optimized for and it is nearly impossible to optimize it in this stage of the development. Factory floor results accumulate fast and in a matter of 5 years, it will be hard for the sql query to want to run given the timeout and optimization you set when you designed the system. A kibana recipe tailored for a mongodb that is in harmony with the software design will be fast even over huge datasets.