This is my field of expertise. Serverless in the sense of lambda/functions is not usable for serious analytics pipelines due to the max allowed image size being smaller than the smallest NLP models or even lightweight analytics python distributions. You can't use lambda on the ETL side and you can't use lambda on the query side unless your queries are trivial enough to be piped straight through to the underlying store. And if your workload is trivial, you should just use clickhouse or straight up postgres because it vastly outperforms serverless stacks in cost and performance[1]
For non-trivial pipelines, tools like spark and dask dominate. And it just so happens that both have plugins to provision their own resources through kubernetes instead of messing around with serverless/paas noise.
And PasS products, well.
https://weekly-geekly.github.io/articles/433346/index.html
>One table instead of 90
>Service requests are executed in milliseconds
>The cost has decreased by half
>Easy removal of duplicate events
Please explain.
[1] https://blog.cloudflare.com/http-analytics-for-6m-requests-p...
IaaS is the peak value proposition of cloud vendors. Serverless/PaaS are grossly overpriced products aimed at non-technical audiences and are mostly snake oil. Change my mind.