Issue Description

Arrow does not seem to perform validation on timestamps for unusual formats where the information conflicts.

The following code snippet runs just fine:

>>> arrow.get("2021-01-30 14:00:00 AM", "YYYY-MM-DD hh:mm:ss A")
<Arrow [2021-01-30T14:00:00+00:00]>

First of all, since hh is documented to go to a maximum value of 12, I expect arrow to raise an err

Describe the bug
Integer columns that are enclosed in quotes are not correctly inferred as integer columns.

Steps/Code to reproduce bug

import cudf
import pandas as pd
from io import StringIO
from cudf.tests.utils import assert_eq

buffer = '"intcol","stringcol"\n"1","some string"\n"2","some other string"'
pd_df = pd.read_csv(StringIO(buffer))
cu_df = cudf.read_csv(String

This will be a breaking change, but will make the API more similar to other tools and improve acceptance.

We no longer need to control the number of concurrent kernels, since now we control the number of concurrent tasks

It's been reported by other projects that musl could lead to performance issues, so we should switch our Linux binary releases to use glibc instead to avoid these kind of potential pitfalls.

It would be helpful to have Fletchgen output warnings for unused metadata fields that start with fletcher_. For example, (this happened to me) when someone adds fletchgen_epc to Schema metadata instead of Field metadata.

Problem description

Reading a dataset with eager's read functionality raises a ValueError when providing columns.

Example code (ideally copy-pastable)

import pandas as pd

from tempfile import TemporaryDirectory
from functools import partial
from storefact import get_store_from_url

from kartothek.io.eager import store_dataframes_as_dataset, read_dataset_as_data