On 11/22/19 2:05 PM, Rémi Cura wrote:
> Hello dear List,
> I'm currently wondering about how to streamline the normalization of a
> new table.
>
> I often have to import messy CSV files into the database, and making
> clean normalized version of these takes me a lot of time (think dozens
> of columns and millions of rows).
To me messy means the information to do the below is not available.
Personally I think you best bet is to get the data into tables and then
use visualization tools to help you determine the below. My guess is
there will be a lot of data cleaning going on before you can get to a
well ordered table layout.
>
> I wrote some code to automatically import a CSV file and infer the type
> of each column.
> Now I'd like to quickly get an idea of
> - what would be the most likely primary key
> - what are the functional dependencies between the columns
>
> The goal is **not** to automate the modelling process,
> but rather to automate the tedious phase of information collection
> that is necessary for the DBA to make a good model.
>
> If this goes well, I'd like to automate further tedious stuff (like
> splitting a table into several ones with appropriate foreign keys /
> constraints)
>
> I'd be glad to have some feedback / pointers to tools in plpgsql or even
> plpython.
>
> Thank you very much
> Remi
>
>
--
Adrian Klaver
adrian.klaver@aklaver.com
