I'm trying to take a raw data set that adds columns for new data and convert it to a more traditional table structure. The idea is to have the script pull the column name (the date) and put that into a new column and then stack each dates data values on top of each other.
Example
Store 1/1/2013 2/1/2013
XYZ INC $1000 $2000
To
Store Date Value
XYZ INC 1/1/2013 $1000
XYZ INC 2/1/2013 $2000
thanks
There are a few different ways that you can get the result that you want.
You can use a SELECT with UNION ALL:
select store, '1/1/2013' date, [1/1/2013] value
from yourtable
union all
select store, '2/1/2013' date, [2/1/2013] value
from yourtable;
See SQL Fiddle with Demo.
You can use the UNPIVOT function:
select store, date, value
from yourtable
unpivot
(
value
for date in ([1/1/2013], [2/1/2013])
) un;
See SQL Fiddle with Demo.
Finally, depending on your version of SQL Server you can use CROSS APPLY:
select store, date, value
from yourtable
cross apply
(
values
('1/1/2013', [1/1/2013]),
('2/1/2013', [2/1/2013])
) c (date, value)
See SQL Fiddle with Demo. All versions will give a result of:
| STORE | DATE | VALUE |
|---------|----------|-------|
| XYZ INC | 1/1/2013 | 1000 |
| XYZ INC | 2/1/2013 | 2000 |
Depending on the details of the problem (i.e. source format, number and variability of dates, how often you need to perform the task, etc), it very well may be much easier to use some other language to parse the data and perform either a reformatting function or the direct insert into the final table.
The above said, if you're interested in a completely SQL solution, it sounds like you're looking for some dynamic pivot functionality. The keywords being dynamic SQL and unpivot. The details vary based on what RDBMS you're using and exactly what the specs are on the initial data set.
I would use a scripting language (Perl, Python, etc.) to generate an INSERT statement for each date column you have in the original data and transpose it into a row keyed by Store and Date. Then run the inserts into your normalized table.
