- It enables self-serve analytics experiences because it creates objects that business people can interact within pivot-table-like forms. Hundreds of lines of SQL are distilled to simple queries.
- The logic is DRYer / easier to govern than all the repetitive SQL rollups that are required to answer business questions.
- Analysts become more productive write the same stuff less and ask more questions faster.
The idea of limiting duplicated logic is very well understood in the software engineering community and desired in the analytics community but we’re still in the early days. In practice, this is really hard in SQL and the tools we have are too limited.
More specifically, the reason why I get excited about MetricFlow
- We basically built a generalized SQL constructor. It will be able to build performant and legible SQL for complicated requests (things that data engineers describe in hundreds of lines) through simple and consistent query interfaces.
- The way we encapsulate logic requires much fewer lines of yaml/code than most other frameworks and we can do much more with those lines. LookML and previous versions we worked on at Airbnb became quite unruly because of the choices in the abstractions.
- The metric abstraction is flexible and allows us to calculate complicated metrics with only a few lines of yaml. That means we can define metrics like conversion metrics that might take joining two data sources together, deduplicating, filtering to a conversion window, etc. in a single way with a few parameters that reference existing objects.
