| 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. |
(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...