An interesting topic that, to me, is nothing new. As a BI engineer something I have worked since I started. The only difference is that it is now ripped out of the BI tool and added additional features. Interesting to see the Evolution of the Semantic Layer 📈 :
1991: SAP BusinessObjects Universe and BI semantic layer
2008: Master Data Management (MDM) (with MDS from Microsoft in 2008)
2013: Kimball discussed the concept of a semantic layer in #158 Making Sense of the Semantic Layer
2016: Maturing BI tools with an integrated semantic layer such as Tableau, TARGIT, PowerBI, Apache Superset, etc. have their own metrics layer definition
2018: Jinja templates and dbt eroding the transformation layer into a semantic layer
2019: Looker and LookML popularized as the first real semantic layer
2022: Modern Semantic Layer, Metric Layer or Headless BI tools such as MetriQL, MetricFlow, Minerva, dbt arose with the explosion of data tools (BI tools, notebooks, spreadsheets, machine learning models, data apps, reverse ETL, …)
We're tackling this with a pretty different angle. Stateful data store managing intelligent connectors. This means you can connect to 3+ business systems, manage data quality/accuracy within those systems (statefully), then produce on-demand joins for a shared metrics library with the new Insights product.
So we vote someone else wins ;) what else are startups for but to chomp at the heels of behemoths? And what else are behemoths-at-war for but to leave an eventually empty battlefield to be painlessly overtaken by a new player?
dbt Metrics are so immature. I think that the definition of metric by dbt shows the constrains their approach has...
dbt Metrics definition:
A metric is a timeseries aggregation over a table that supports zero or more dimensions.
The need for time dimension and aggregation over a (single) table is very limiting.
I recommend checking the following article: https://medium.com/gooddata-developers/gooddata-and-dbt-metrics-aa8edd3da4e3
The article compares GoodData metrics with dbt metrics, and it is really interesting to see the key differences.
An interesting topic that, to me, is nothing new. As a BI engineer something I have worked since I started. The only difference is that it is now ripped out of the BI tool and added additional features. Interesting to see the Evolution of the Semantic Layer 📈 :
1991: SAP BusinessObjects Universe and BI semantic layer
2008: Master Data Management (MDM) (with MDS from Microsoft in 2008)
2013: Kimball discussed the concept of a semantic layer in #158 Making Sense of the Semantic Layer
2016: Maturing BI tools with an integrated semantic layer such as Tableau, TARGIT, PowerBI, Apache Superset, etc. have their own metrics layer definition
2018: Jinja templates and dbt eroding the transformation layer into a semantic layer
2019: Looker and LookML popularized as the first real semantic layer
2022: Modern Semantic Layer, Metric Layer or Headless BI tools such as MetriQL, MetricFlow, Minerva, dbt arose with the explosion of data tools (BI tools, notebooks, spreadsheets, machine learning models, data apps, reverse ETL, …)
More on https://airbyte.com/blog/the-rise-of-the-semantic-layer-metrics-on-the-fly in case of interest.
"I can’t be sure but iirc dbt’s syntax looks really suspiciously like Supergrain’s did before they pivoted."
LOL pull George in here ;)
lol "something something data mesh"
We're tackling this with a pretty different angle. Stateful data store managing intelligent connectors. This means you can connect to 3+ business systems, manage data quality/accuracy within those systems (statefully), then produce on-demand joins for a shared metrics library with the new Insights product.
http://syncari.com/insights/
https://syncari.com/product/
So we vote someone else wins ;) what else are startups for but to chomp at the heels of behemoths? And what else are behemoths-at-war for but to leave an eventually empty battlefield to be painlessly overtaken by a new player?