Database Normalization

What is Database Normalization?

Database normalization is the process of organizing data into tables in such a way that the results of using the database are always unambiguous and as intended. Such normalization is intrinsic to relational database theory. It may have the effect of duplicating data within the database and often results in the creation of additional tables

The concept of database normalization is generally traced back to E.F. Codd, an IBM researcher who, in 1970, published a paper describing the relational database model. What Codd described as "a normal form for database relations" was an essential element of the relational technique. Such data normalization found a ready audience in the 1970s and 1980s -- a time when disk drives were quite expensive and a highly efficient means for data storage was very necessary. Since that time, other techniques, including de-normalization, have also found favor.

While data normalization rules tend to increase the duplication of data, it does not introduce data redundancy, which is unnecessary duplication. Database normalization is typically a refinement process after the initial exercise of identifying the data objects that should be in the relational database, identifying their relationships and defining the tables required and the columns within each table.

Simple data normalization example

Customer	Item purchased	Purchase price
Ibad	Shirt	Rs.400
Yusra	Tennis shoes	Rs.350
Umer	Shirt	Rs.400
Fatima	Trousers	Rs.250

If this table is used for the purpose of keeping track of the price of items and you want to delete one of the customers, you will also delete the price. Normalizing the data would mean understanding this and solving the problem by dividing this table into two tables, one with information about each customer and the product they bought and the second with each product and its price. Making additions or deletions to either table would not affect the other.

Normalization degrees of relational database tables have been defined and include:

First normal form (1NF).

This is the "basic" level of database normalization, and it generally corresponds to the definition of any database, namely:

·         It contains two-dimensional tables with rows and columns.

·         Each column corresponds to a sub object or an attribute of the object represented by the entire table.

·         Each row represents a unique instance of that sub object or attribute and must be different in some way from any other row (that is, no duplicate rows are possible).

·         All entries in any column must be of the same kind. For example, in the column labeled "Customer," only customer names or numbers are permitted.

Second normal form (2NF). 

At this level of normalization, each column in a table that is not a determiner of the contents of another column must itself be a function of the other columns in the table. For example, in a table with three columns containing the customer ID, the product sold and the price of the product when sold, the price would be a function of the customer ID (entitled to a discount) and the specific product.

Third normal form (3NF).

At the second normal form, modifications are still possible because a change to one row in a table may affect data that refers to this information from another table. For example, using the customer table just cited, removing a row describing a customer purchase (because of a return, perhaps) will also remove the fact that the product has a certain price. In the third normal form, these tables would be divided into two tables so that product pricing would be tracked separately.

Extensions of basic normal forms include the domain/key normal form, in which a key uniquely identifies each row in a table, and the Boyce-Codd normal form, which refines and enhances the techniques used in the 3NF to handle some types of anomalies.

Database normalization's ability to avoid or reduce data anomalies, data redundancies and data duplication's, while improving data integrity, have made it an important part of the data developer's toolkit for many years. It has been one of the hallmarks of the relational data model.

The relational model arose in an era when business records were, first and foremost, on paper. Its use of tables was, in some part, an effort to mirror the type of tables used on paper that acted as the original representation of the (mostly accounting) data. The need to support that type of representation has waned as digital-first representations of data have replaced paper-first records.

But other factors have also contributed to challenging the dominance of database normalization.

Over time, continued reductions in the cost of disk storage, as well as new analytical architectures, have cut into normalization's supremacy. The rise of de-normalization as an alternative began in earnest with the advent of data warehouses, beginning in the 1990s. More recently, document-oriented No-SQL databases have arisen; these and other non-relational systems often tap into non-disk-oriented storage types. Now, more than in the past, data architects and developers balance data normalization and de-normalization as they design their systems.

Normalization with Example

Why do we need to do normalization?

To eliminate redundancy of data i.e. having same information stored at multiple places, which eventually be difficult to maintain and will also increase the size of our database.

With normalization we will have tables with fewer columns which will make data retrieval and insert, update and delete operations more efficient.

What do we mean when we say a table is not in normalized form?

Let’s take an example to understand this,

Say I want to create a database which stores my friends name and their top three favorite artists.

This database would be quite a simple so initially I’ll be having only one table in it say friends table. Here FID is the primary key.

FID	FNAME	FavoriteArtist
1	Srihari	Akon, The Corrs, Robbie Williams.
2	Arvind	Enigma, Chicane, Shania Twain

This table is not in normal form why?

Favorite Artist column is not atomic or doesn’t have scalar value i.e. it has having more than one value.

Let’s modify this table

FID	FNAME	FavoriteArtist1	FavoriteArtist2	FavoriteArtist3
1	Srihari	Akon.	The Corrs	Robbie Williams.
2	Arvind	Enigma	Chicane	Shania Twain

This table is also not in normal form why?

We have now changed our table and now each column has only one value!! (So what’s left?)

Because here we are having multiple columns with same kind of value.

I.e. repeating group of data or repeating columns.

So what we need to do to make it normal or at least bring it in First Normal Form?

We’ll first break our single table into two.

Each table should have information about only one entity so it would be nice if we store our friend’s information in one table and his favorite artists’ information in another

(For simplicity we are working with few columns but in real world scenario there could be column like friend’s phone no, email , address and favorites artists albums, awards received by them, country etc. So in that case having two different tables would make complete sense)

FID	FNAME
1	Srihari
2	Arvind

FID	Favorite Artist
1	Akon.
1	The Corrs
1	Robbie Williams
2	Enigma
2	Chicane
2	Shania Twain

FID foreign key in FavoriteArtist table which refers to FID in our Friends Table.

Now we can say that our table is in first normal form.

Remember For First Normal Form

1...Column values should be atomic, scalar or should be holding single value

2...No repetition of information or values in multiple columns.

3...So what does Second Normal Form means?

Second normal form our database should already be in first normal form and every non-key column must depend on entire primary key.

Here we can say that our Friend database was already in second normal form l.

Why?

Because we don’t have composite primary key in our friends and favorite artists table.

Composite primary keys are- primary keys made up of more than one column. But there is no such thing in our database.

But still let’s try to understand second normal form with another example

This is our new table

Gadgets	Supplier	Cost	Supplier Address
Headphone	Abaci	123$	New York
Mp3 Player	Sagas	250$	California
Headphone	Mayas	100$	London

In about table ITEM+SUPPLIER together form a composite primary key.

Let’s check for dependency

If I know gadget can I know the cost?

No same gadget is provided my different supplier at different rate.

If I know supplier can I know about the cost?

No because same supplier can provide me with different gadgets.

If I know both gadget and supplier can I know cost?

Yes than we can.

So cost is fully dependent (functionally dependent) on our composite primary key (Gadgets+Supplier)

Let’s start with another non-key column Supplier Address.

If I know gadget will I come to know about supplier address?

Obviously no.

If I know who the supplier is can I have it address?

Yes.

So here supplier is not completely dependent on (partial dependent) on our composite primary key (Gadgets + Supplier).

This table is surely not in Second Normal Form.

So what do we need to do to bring it in second normal form?

Here again we’ll break the table in two.

Gadgets	Supplier	Cost
Headphone	Abaci	123$
Mp3 Player	Sagas	250$
Headphone	Mayas	100$

Supplier	Supplier Address
Abaci	New York
Sagas	California
Mayas	London

We know how to normalize till second normal form.

But let’s take a break over here and learn some definitions and terms.

Composite Key: -Composite key is a primary key composed of multiple columns.

Functional Dependency – When value of one column is dependent on another column.

So that if value of one column changes the value of other column changes as well.

E.g. Supplier Address is functionally dependent on supplier name. If supplier’s name is changed in a record we need to change the supplier address as well.

S.Supplier – àS.SupplierAddress

“In our s table supplier address column is functionally dependent on the supplier column”

Partial Functional Dependency – A non-key column is dependent on some, but not all the columns in a composite primary key.

In our above example Supplier Address was partially dependent on our composite key columns (Gadgets + Supplier).

Transitive Dependency- A transitive dependency is a type of functional dependency in which the value in a non-key column is determined by the value in another non-key column.

With these definitions in mind let’s move to Third Normal Form.

For a table in third normal form

·         It should already be in Second Normal Form.

·         There should be no transitive dependency, i.e. we shouldn’t have any non-key column depending on any other non-key column.

Again we need to make sure that the non-key columns depend upon the primary key and not on any other non-key column.

Album	Artist	No. of tracks	Country
Come on over	Shania Twain	11	Canada
History	Michael Jackson	15	USA
Up	Shania Twain	11	Canada
MCMXC A.D.	Enigma	8	Spain
The cross of changes	Enigma	10	Spain

Although the above table looks fine but still there is something in it because of which we will normalize it further.

Album is the primary key of the above table.

Artist and No. of tracks are functionally dependent on the Album(primary key).

But can we say the same of Country as well?

In the above table Country value is getting repeated because of artist.

So in our above table Country column is depended on Artist column which is a non-key column.

So we will move that information in another table and could save table from redundancy i.e. repeating values of Country column.

Album	Artist	No. of tracks
Come on over	Shania Twain	11
History	Michael Jackson	15
Up	Shania Twain	11
MCMXC A.D.	Enigma	8
The cross of changes	Enigma	10

Artist	Country
Shania Twain	Canada
Michael Jackson	USA
Enigma	Spain

Normally this is considered enough and we don’t really go on applying the other normal forms.

Most of real-world application has databases which are in third normal forms.