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YeongHer's avatar

My project: https://github.com/Agile-Data/flat-ql

It compiles flat query language into SQL dialect of various database for data analysis.

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Arjun Saksena's avatar

Great blog post Amit.

At Humanic, we further believe that the metrics layer needs to be integrated within a context of a tool that the business owner (Revenue Leader) uses to take some revenue generating action, otherwise it becomes yet another tool that Data Scientists and Business Analysts will be utilizing.

That by itself is not a bad idea except that much is lost in translation when a business user needs to communicate data requirements to a Data Scientist.

Would love to get a coffee to discuss further! saksena@humanic.ai

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Dev Bhosale's avatar

Amit, Excellent article and thanks for sharing your thoughts. I agree that there are benefits of unbundling the metrics layers and solution #3 is promising.

If solution #3 were adopted by a few vendors, the next question is, what will be the bridge between visualization grammar and the query generation tool.

Generation of queries for multiple aggregation levels is non-trivial. What if the tool vendor wishes to optimize the query created by a separate layer?

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iCode's avatar

Merits of metrics! 🙌 🎉

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May 13, 2022
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Amit Prakash's avatar

Excellent points Dan! Could you elaborate more about the graph modeling?

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