Don't think he is that wrong. Putting aside Spark, the remaining terms are pretty broad.
Data Lakes: Is this our new fancy term describing "data". So what distinguishes "data lakes" from "data"?
ETL: I would guess 95% of application programs take input, parse it (extract), do some data wrangling (transform) and save the result somewhere else (load).
Validation: Again a broad term. Do you mean validation of statistical models? Without validation your predictions are worthless so I guess it is a standard thing to do if you want to do any kind of machine learning.
Schema Management and data catalogs: Standard DB stuff I would say.
We just like to define new job descriptions. It's the same with DevOps, which seems to be the new term for System Administrator.
Data Lakes: Is this our new fancy term describing "data". So what distinguishes "data lakes" from "data"?
ETL: I would guess 95% of application programs take input, parse it (extract), do some data wrangling (transform) and save the result somewhere else (load).
Validation: Again a broad term. Do you mean validation of statistical models? Without validation your predictions are worthless so I guess it is a standard thing to do if you want to do any kind of machine learning.
Schema Management and data catalogs: Standard DB stuff I would say.
We just like to define new job descriptions. It's the same with DevOps, which seems to be the new term for System Administrator.