I buy into some of the hype. but only for majority use cases, not for edge case specialisation.
As ever, at FANGMA-whatever scale/use cases, yeah I’d agree with you. But the majority of cases are not FANGMA-whatever scale/use cases.
Basically, it’s good enough for most people. Plus it takes away a bunch of complexity for them.
> If the application has sustained query load
Analytical queries in majority cases are not causing sustained load.
It’s a few dashboards a handful of managers/teams check a couple of times through the day.
Or a few people crunching some ad hoc queries (and hopefully writing the intermediate results somewhere so they don’t have to keep making the same query — I.e. no sustained load problem).
> real-time data ingestion requirements
Most of the time a nightly batch job is good enough. Most Businesses still work on day by day or week by week, and that’s at the high frequency end of things.
> slow innovation speed
Most people don’t want bleeding edge innovative change. They want stability.
Data engineers have enough problems with teams changing source database fields without telling us. We don’t need the tool we’re storing the data with to constantly break too.
As ever, at FANGMA-whatever scale/use cases, yeah I’d agree with you. But the majority of cases are not FANGMA-whatever scale/use cases.
Basically, it’s good enough for most people. Plus it takes away a bunch of complexity for them.
> If the application has sustained query load
Analytical queries in majority cases are not causing sustained load.
It’s a few dashboards a handful of managers/teams check a couple of times through the day.
Or a few people crunching some ad hoc queries (and hopefully writing the intermediate results somewhere so they don’t have to keep making the same query — I.e. no sustained load problem).
> real-time data ingestion requirements
Most of the time a nightly batch job is good enough. Most Businesses still work on day by day or week by week, and that’s at the high frequency end of things.
> slow innovation speed
Most people don’t want bleeding edge innovative change. They want stability.
Data engineers have enough problems with teams changing source database fields without telling us. We don’t need the tool we’re storing the data with to constantly break too.