[nicholashandel]

Traditionally, querying business and product metrics for data analysis has required lots of ad-hoc sql queries. Often they are encoded in dashboards, in ETL pipelines, and others are copied directly. Semantic layers act as a single source of truth that encapsulates all of that logic. Having a single layer responsible for querying these datasets enables powerful workflows:

- 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.

[pstoll]

Well said!

We need more of this problem space exposed to engineers and not just for “analysts”.

I’ll share a couple other articles from a company that does a nice job explaining the technical problems in what is traditionally “business analytics”.

The space is OLAP and you may have scoffed at the idea of “OLAP cubes”, but man were they useful. In the way that excel powers a ton of business processes, cubes powered a lot of analytics. Underlying tech is cool but they are showing their age: https://www.holistics.io/blog/the-rise-and-fall-of-the-olap-...

Another write up of this idea of a semantic layer above raw sql statement: https://www.holistics.io/blog/holistics-data-modeling-explai...

So this “semantic layer” leverages the latest tech to deliver the same business insights faster, better, more flexibly. Ie once you define this semantic layer over your data (ie how all your sql tables are connected), the semantic engine knows how to query up and down your data model, writing the SQL queries for you, on the fly. You can ask and answer new questions without writing new queries. And with modern columnar query engines (eg big query, spark, presto, etc), perf is usually pretty good.

[pstoll]

And for completeness, here’s another company that I also used at $previous_job that provides a “semantic model” offering. This write up also helps describe where it fits in.

(This one has just enough content vs marketing for me not to feel embarrassed posting here on HN for people who want to find out more. And IMO the BI landscape is littered with pablum from an engineers POV, often obscuring the nature of the technical problems to be solved in the space - which are very cool.)

https://www.atscale.com/blog/what-is-a-universal-semantic-la...