Im a novice to Databases, and I am currently trying to turn a spreadsheet of an inventory of IT equipment, etc. into a database as the spreadsheet is getting to large to handle.

I computer literate and have no problems with how to use a computer at all but I do not know how to normalize. I have looked but am unable to find a step by step guide to normalization. All i have been able to find is a guide and the reasons for each stage but no actual examples which I need because I have tried myself and failed.

Any help is welcome.

Thanks, Dylan

Install Mysql + Phpmyadmin to achieve what you want. Phpmyadmin is a (simple) yet great front-end tool for mysql queries among many other database tasks. Once you get it working, you will need to say a prayer to keep phpmyadmin from 'resetting' your password.

Originally Posted by Brian07002

Install Mysql + Phpmyadmin to achieve what you want. Phpmyadmin is a (simple) yet great front-end tool for mysql queries among many other database tasks. Once you get it working, you will need to say a prayer to keep phpmyadmin from 'resetting' your password.

I don't think that really answers his question. Though I agree that he should convert his spreadsheet to a SQL database, I don't think phpmyadmin will help him normalize his data.

@i8grand

Normalization just ensures that your data can be stored in a relational model. More or less, it is a systematic way of removing inconsistencies from your database. Say you have a table and it has 3 columns (c1 c2 c3). If a tuple has only values c1 and c2. If c3 is specified as not null or is a primary key then you have a problem.

Typically this is something that can easily occur in a less strict data model such as xml. In xml a relation might have multiple instances of a column value or no instance at all.

Spreadsheets, however, are already very close to a relational model since they are typically tables. Unless you have lots of tubles that are missing column values or somehow have more than one (in a single row) then you shouldn't really need to worry about normalization too much, but I cannot say for sure without seeing an example of your data.