Query Snowflake Named Internal Stage by Column NAME and not POSITION - snowflake-cloud-data-platform

Question

My company is attempting to use Snowflake Named Internal Stages as a data lake to store vendor extracts.
There is a vendor that provides an extract that is 1000+ columns in a pipe delimited .dat file. This is a canned report that they extract. The column names WILL always remain the same. However, the column locations can change over time without warning.
Based on my research, a user can only query a file in a named internal stage using the following syntax:
--problematic because the order of the columns can change.
select t.$1, t.$2 from #mystage1 (file_format => 'myformat', pattern=>'.data.[.]dat.gz') t;
Is there anyway to use the column names instead?
E.g.,
Select t.first_name from #mystage1 (file_format => 'myformat', pattern=>'.data.[.]csv.gz') t;
I appreciate everyone's help and I do realize that this is an unusual requirement.

Answer 1

You could read these files with a UDF. Parse the CSV inside the UDF with code aware of the headers. Then output either multiple columns or one variant.
For example, let's create a .CSV inside Snowflake we can play with later:
create or replace temporary stage my_int_stage
file_format = (type=csv compression=none);
copy into '#my_int_stage/fx3.csv'
from (
select *
from snowflake_sample_data.tpcds_sf100tcl.catalog_returns
limit 200000
)
header=true
single=true
overwrite=true
max_file_size=40772160
;
list #my_int_stage
-- 34MB uncompressed CSV, because why not
;
Then this is a Python UDF that can read that CSV and parse it into an Object, while being aware of the headers:
create or replace function uncsv_py()
returns table(x variant)
language python
imports=('#my_int_stage/fx3.csv')
handler = 'X'
runtime_version = 3.8
as $$
import csv
import sys
IMPORT_DIRECTORY_NAME = "snowflake_import_directory"
import_dir = sys._xoptions[IMPORT_DIRECTORY_NAME]
class X:
def process(self):
with open(import_dir + 'fx3.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
yield(row, )
$$;
And then you can read this UDF that outputs a table:
select *
from table(uncsv_py())
limit 10
A limitation of what I showed here is that the Python UDF needs an explicit name of a file (for now), as it doesn't take a whole folder. Java UDFs do - it will just take longer to write an equivalent UDF.
https://docs.snowflake.com/en/developer-guide/udf/python/udf-python-tabular-functions.html
https://docs.snowflake.com/en/user-guide/unstructured-data-java.html