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@xxxxxxxxxxx
