Check existing columns and seperate data processing in two parts

Question

Hi,

I think it's a frequenlty ask but I didn't manage to solve the problem, after reading differents solutions.

So, I'am trying to explain the difficult I have.

I built a worflow to download historical datas from csv files. I noticed that some columns were the same but with a different name, so I did some manipulations to rename and adjust the correct columns.

Then, this datas are saved in Database. So far, everything is fine.

But now, when I am using this worflow I don't need to download the historical datas but only recent datas.

So, all manipulations I have done with certainly columns are not existing in the new datas, and the workflow is not valid, there are missing columns.

So, I would like to separate the workflow in two parts :

* one part would be if I want to download datas from historical with problematic columns
* Second part, if I want only download recent datas where there are not theese problematic columns.

I don't know how to do this, I've tried many tools like Detour tool, Message tools, Block tools.... but I don't succeed.

Any help you can give me would be greatly appreciated.

Thank you.

ArtApa · Answer

Hi @Vanderleck16 - You can still use Detour, but you will need to hide it in a Macro. Here is an example:

When historical then you tick the box, when not then you just leave the box empty.

Your macro will look something like this:

L - Without additional steps. R - for historical records with additional steps.

I attached the package for your convenience.

Hope that helps.

DetourSample.yxzp

MattBSlalom · Answer

Are your columns in the same order just with various names?  If so, you could bring in the data and uncheck the "First Row Contains Field Names" option so the column headers are generic "Field_1", "Field_2", etc.  Then also change the value of "Start Data Import on Line" to line 2 to skip the field name row in the CSV.

Separately, you can have an input from a static source with the desired field names to be unioned on top of the 1st dataset for use in the Dynamic Rename.