Last time I checked, enterprise ETL tools were sold as capable of a lot more than simple OLAP to OLTP. I find the reality provided is somewhat underwhelming. Given that facebook can tell the difference between a photo of Dave and one of Jim, why do I have to manually provide a mask for every single date field flowing through an enterprise?
If it matters anywhere it matters in the data space. We don't see the disconnect between decimal and int, but when you're expecting a character and you get varchar, (not sure about the apostrophe case, but I suspect your talking about quotes and embedded commas) and the number of fields or composition of fields changes (e.g. col1:sttring "jack, dorsey" col2:int 156 and the parser sees col1:string "jack" col2: "dorsey, 156" you want to know that is broken ASAP.
Double.parseDouble(x) == Double.parseDouble(y)
/* instead of pythons */
x == y
27 times to get a feed file parsed, then I'd say that the tools are lacking basic useability. And especially primitive in light of the handwriting reading, photo tagging, go playing,supermario winning possibilities of ML.And ideally if something makes it through the data filtering layer into the logic layer and does not make sense there, then that should be handled. And that's where strong types help. It forces you to handle these cases, even if that means logging/alerting/ignoring, but at least you'll have to make a decision when you write the logic, instead of 3AM in the morning.
Any CSV you're working with should be properly escaped anyway or you're bound for a world of pain.