Oracle added 'Analytic Views' feature in its database in 2017, starting with version 12.2 of database. It evolved from OLAP engine inside Oracle database, which used to be 'EXPRESS' multidimensional engine in 1980s and 1990s.
To me this is just lingo (or should I say just semantics). Data Dictionaries along with Conceptual or Logical Data Models basically serve the same purpose. Tell me if I am wrong.
I really enjoyed the end: without a match between technology and stakeholders, engineers cannot do much! The semantic layer tools can only alleviate this burden... hopefully!
An important point that I think you've missed, my friend:
The Semantic Layer was indeed vendor lock-in BS as you described.
However, with the emergence of Natural Language BI as the holy grail, it appears there's no trustworthy way of launching it without some Semantic Layer that feeds the agent.
I really should read the rest (skimmed), but you lost me at this point in terms of an argument. These aren't definitions that "differ significantly" IMO. One is more broad/general (DuckDB) than the other (Databricks).
Interestingly, if you are an astute observer, you will notice that DuckDB and Databricks actually differ significantly in their definitions of a Semantic Layer.
Databricks says it’s AFTER Data Lakes, Data Marts, but BEFORE the BI tools.
DuckDB says it’s simply something AFTER the database and BEFORE the business user.
popcorn as a poll option is my top highlight
Oracle added 'Analytic Views' feature in its database in 2017, starting with version 12.2 of database. It evolved from OLAP engine inside Oracle database, which used to be 'EXPRESS' multidimensional engine in 1980s and 1990s.
As I have worked with Snowflake Semantic Views and Oracle 'Analytic Views' (AV), I have found that Oracle AV syntax is much easier and it has more features than Snowflake Semantic Views. https://docs.oracle.com/en/database/oracle/oracle-database/23/dwhsg/overview-analytic-views.html There are some blogs and YouTube videos about this excellent feature also.
To me this is just lingo (or should I say just semantics). Data Dictionaries along with Conceptual or Logical Data Models basically serve the same purpose. Tell me if I am wrong.
I really enjoyed the end: without a match between technology and stakeholders, engineers cannot do much! The semantic layer tools can only alleviate this burden... hopefully!
hilarious
An important point that I think you've missed, my friend:
The Semantic Layer was indeed vendor lock-in BS as you described.
However, with the emergence of Natural Language BI as the holy grail, it appears there's no trustworthy way of launching it without some Semantic Layer that feeds the agent.
No wonder Snowflake tries to standardize this mess into a single semantic syntax - https://www.snowflake.com/en/blog/open-semantic-interchange-ai-standard/
I really should read the rest (skimmed), but you lost me at this point in terms of an argument. These aren't definitions that "differ significantly" IMO. One is more broad/general (DuckDB) than the other (Databricks).
Interestingly, if you are an astute observer, you will notice that DuckDB and Databricks actually differ significantly in their definitions of a Semantic Layer.
Databricks says it’s AFTER Data Lakes, Data Marts, but BEFORE the BI tools.
DuckDB says it’s simply something AFTER the database and BEFORE the business user.