I apologize if this may seem like somewhat of a novice question (which it probably is), but I'm just introducing myself to the idea of relational databases and I'm struggling with this concept.
I have a database with roughly 75 fields which represent different characteristics of a 'user'. One of those fields represents a the locations that user has been and I'm wondering what the best way is to store the data so that it is easily retrievable and can be used later on (i.e. tracking a route on Google Maps, identifying if two users shared the same location etc.)
The problem is that some users may have 5 locations in total while others may be well over 100.
Is it best to store these locations in a text file named using the unique id of each user(one location on each line, or in a csv)?
Or to create a separate table for each individual user connected to their unique id (that seems like overkill to me)?
Or, is there a way to store all of the locations directly in the single field in the original table?
I'm hoping that I'm missing a concept, or there is a link to a tutorial that will help my understanding.
If it helps, you can assume that the locations will be stored in order and will not be changed once stored. Also, these locations are static (I don't need to add any more locations once as they can't be updated).
Thank you for time in helping me. I appreciate it!
Store the location data for the user in a separate table. The location table would link back to the user table by a common user_id.
Keeping multiple locations for a particular user in a single table is not a good idea - you'll end up with denormalized data.
You may want to read up on:
Referential Integrity
Relational denormalization
The most common way would be to have a separate table, something like
USER_LOCATION
+------------+------------------+
| USER_ID | LOCATION_ID |
+------------+------------------+
| | |
If user 3 has 5 locations, there will be five rows containing user_id 3.
However, if you say the order of locations matter then an additional field specifying the ordinal position of the location within a user can be used.
The separate table approach is what we call normalized.
If you store a location list as a comma-separated string of location ids, for example, it is trival to maintain the order, but you lose the ability for the database to quickly answer the question "which users have been at location x?". Your data would be what we call denormalized.
You do have options, of course, but relational databases are pretty good with joining tables, and they are not overkill. They do look a little funny when you have ordering requirements, like the one you mention. But people use them all the time.
In a relational database you would use a mapping table. So you would have user, location and userlocation tables (user is a reserved word so you may wish to use a different name). This allows you to have a many-to-many relationship, i.e. many users can visit many locations. If you want to model a route as an ordered collection of locations then you will need to do more work. This site gives an example
Related
To keep this as short as possible I'm going to use and example.
So let's say I have a simple database that has the following tables:
company - ( "idcompany", "name", "createdOn" )
user - ( "iduser", "idcompany", "name", "dob", "createdOn" )
event - ( "idevent", "idcompany", "name", "description", "date", "createdOn" )
Many users can be linked to a single company as well as multiple events and many events can be linked to a single company. All companies, users and events have columns as show above in common. However, what if I wanted to give my customers the ability to add custom fields to both their users and their events for any unique extra information they wish to store. These extra fields would be on a company wide basis, not on a per record basis ( so a company adding a custom field to their users would add it to all of their users not just one specific user ). The custom fields also need to be sesrchable and have the ability to be reported on, ideally automatically with some sort of report wizard. Considering the database is expected to have lots of traffic as well as lots of custom fields, what is the best solution for this?
My current research and findings in possible solutions:
To have generic placeholder columns such as "custom1", "custom2" etc.
** This is not viable as there will eventually be too many custom columns and there will be too many NULL values stored in the database
To have 3x tables per current table. eg: user, user-custom-field, user-custom-field-value. The user table being the same. The user-custom-field table containing the information about the new field such as name, data type etc. And the user-custom-field-value table containing the value for the custom field
** This one is more of a contender if it were not for its complexity and table size implications. I think it will be impossible to avoid a user-custom-field table if I want to automatically report on these fields as I will have to store the information on how to report on these fields here. However, In order to pull almost any data you would have to do a million joins on the user-custom-field-value table as well as the fact that your now storing column data as rows which in a database expected to have a lot of traffic as well as a lot of custom fields would soon cause a problem.
Create a new user and event table for each new company that is added to the system removing the company id from within those tables and instead using it in the table name ( eg user56, 56 being the company id ). Then allowing the user to trigger DB commands that add the new custom columns to the tables giving them the power to decide if it has a default value or auto increments etc.
** Everytime I have seen this solution it has always instantly been shut down by people saying it would be unmanageable as you would eventually get thousands of tables. However nobody really explains what they mean by unmanageable. Firstly as far as my understanding goes, more tables is actually more efficient and produces faster search times as the tables are much smaller. Secondly, yes I understand that making any common table changes would be difficult but all you would have to do is run a script that changes all your tables for each company. Finally I actually see benefits using this method as it would seperate company data making it impossible for one to accidentally access another's data via a potential bug, plus it would potentially give the ability to back up and restore company data individually. If someone could elaborate on why this is perceived as a bad idea It would be appreciated.
Convert fully or partially to a NoSQL database.
** Honestly I have no experience with schemaless databases and don't really know how dynamic user defined fields on a per record basis would work ( although I know it's possible ). If someone could explain the implications of the switch or differences in queries and potential benefits that would be appreciated.
Create a JSON column in each table that requires extra fields. Then add the extra fields into that JSON object.
** The issue I have with this solution is that it is nearly impossible to filter data via the custom columns. You would not be able to report on these columns and until you have received and processed them you don't really know what is in them.
Finally if anyone has a solution not mentioned above or any thoughts or disagreements on any of my notes please tell me as this is all I have been able to find or figure out for myself.
A typical solution is to have a JSON (or XML) column that contains the user-defined fields. This would be an additional column in each table.
This is the most flexible. It allows:
New fields to be created at any time.
No modification to the existing table to do so.
Supports any reasonable type of field, including types not readily available in SQL (i.e. array).
On the downside,
There is no validation of the fields.
Some databases support JSON but do not support indexes on them.
JSON is not "known" to the database for things like foreign key constraints and table definitions.
So, I've read a lot about how stashing multiple values into one column is a bad idea and violates the first rule of data normalisation (which, surprisingly, is not "Do Not Talk About Data Normalisation") so I need some help.
At the moment I'm designing an ASP .NET webpage for the place I work for. I want to display data on a web page depending on what Active Directory groups the person belongs to. The first way of doing this that comes to mind is to have a table with, essentially, a column containing the AD group and the second column containing what list of computers belong to that list.
I've learnt that this is showing great disregard for relational databases, so what is a better way to do it? I want to control this access by SQL tables, so I can add/remove from these tables and change end users access accordingly.
Thanks for the help! :)
EDIT: To describe exactly what I want to do is this:
We have a certain group of computers that need to be checked up on, however these computers are in physically difficult to reach locations. The organisation I belong to has remote control enabled for these computers, however they're not in the business of giving out the remote control password (understandable).
The added layer of complexity is that, depending on who you are, our clients should only be able to see a certain group of computers (that is, the group of computers that their area owns). So, if Group A has Thomas in it, and Group B has Jones in it, if you belong to either group then you would just see one entry. However, if you belong to both groups you should see both Thomas and Jones computers in it.
The reason why I think that storing this data in a SQL cell is the way to go is because, to store them in tables would require (in my mind) a new table for each new "group" of computers. I don't want to crank out SQL tables for every new group, I'd much rather just have an added row in a SQL table somewhere.
Does this make any sense?
You basically have three options in SQL Server:
Storing the values in a single column.
Storing the values in a junction table.
Storing the values as XML (or as some other structured data format).
(Other databases have other options, such as arrays, nested tables, and JSON.)
In almost all cases, using a junction table is the correct approach. Why? Here are some reasons:
SQL Server has (relatively) lousy string manipulation, so doing something as simple as ensuring a unique list is really, really hard.
A junction table allows you to store lots of other information (When was a machine added? What is the full description of the machine? etc. etc.).
Most queries that you want are pretty easy with a junction table (with the one exception of getting a comma-delimited list, alas -- which is just counterintuitive rather than "hard").
All the types are stored natively.
A junction table allows you to enforce constraints (both check and foreign key) on the elements of the list.
Although a delimited list is almost never the right solution, it is possible to think of cases where it might be useful:
The list doesn't change and presentation of the list is very important.
Space usage is an issue (alas, denormalization often results in fewer pages).
Queries do not really access elements of the list, just the entire thing.
XML is also a reasonable choice under some circumstances. In the most recent versions of SQL Server, this can be made pretty efficient. However, it incurs the overhead of reading and parsing XML -- and things like duplicate elimination are still not obvious.
So, you do have options. In almost all cases, the junction table is the right approach.
There is an "it depends" that you should consider. If the data is never going to be queried (or queried very rarely) storing it as XML or JSON would be perfectly acceptable. Many DBAs would freak out but it is much faster to get the blob of data that you are going to send to the client than to recompose and decompose a set of columns from a secondary table. (There is a reason document and object databases are becoming so popular.)
... though I would ask why are you replicating active directory to your database and how are you planning on keeping these in sync.
I not really a bad idea to store multiple values in one column, but will depend the search you want.
If you just only want to know the persons that is part of a group then you can store persons in one column with a group id as key. For update you just update the entire list in a group.
But if you want to search a specified person that belongs to group, then its not recommended that you store this multiple persons in one column. In this case its better to store a itermedium table that store person id, and group id.
Sounds like you want a table that maps users to group IDs and a second table that maps group IDs to which computers are in that group. I'm not sure, your language describing the problem was a bit confusing to me.
a list has some columns like: name, family name, phone number etc.
and rows like name=john familyName= lee number=12321321
name=... familyname=... number=...
an sql database works same way. every row in a sql database is a record. so you jusr add records of your list into your database using insert query.
complete explanation in here:
http://www.w3schools.com/sql/sql_insert.asp
This sounds like a typical many-to-many problem. You have many groups and many computers and they are related to eachother. In this situation, it is often recommended to use a mapping table, a.k.a. "junction table" or "cross-reference" table. This table consist solely of the two foreign keys in your other tables.
If your tables look like this:
Computer
- computerId
- otherComputerColumns
Group
- groupId
- othergroupColumns
Then your mapping table would look like this:
GroupComputer
- groupId
- computerId
And you would insert a single record for every relationship between a group and computer. This is in compliance with the rules for third normal form in regards to database normalization.
You can have a table with the group and group id, another table with the computer and computer id and a third table with the relation of group id and computer id.
I'm new to databases and I'm thinking of creating one for a website. I started with SQL, but I really am not sure if I'm using the right kind of database.
Here's the problem:
What I have right now is the first option. So that means that, my query looks something like this:
user_id photo_id photo_url
0 0 abc.jpg
0 1 123.jpg
0 2 lol.png
etc.. But to me that seems a little bit inefficient when the database becomes BIG. So the thing I want is the second option shown in the picture. Something like this, then:
user_id photos
0 {abc.jpg, 123.jpg, lol.png}
Or something like that:
user_id photo_ids
0 {0, 1, 2}
I couldn't find anything like that, I only find the ordinary SQL. Is there anyway to do something like that^ (even if it isn't considered a "database")? If not, why is SQL more efficient for those kinds of situations? How can I make it more efficient?
Thanks in advance.
Your initial approach to having a user_id, photo_id, photo_url is correct. This is the normalized relationship that most database management systems use.
The following relationship is called "one to many," as a user can have many photos.
You may want to go as far as separating the photo details and just providing a reference table between the users and photos.
The reason your second approach is inefficient is because databases are not designed to search or store multiple values in a single column. While it's possible to store data in this fashion, you shouldn't.
If you wanted to locate a particular photo for a user using your second approach, you would have to search using LIKE, which will most likely not make use of any indexes. The process of extracting or listing those photos would also be inefficient.
You can read more about basic database principles here.
Your first example looks like a traditional relational database, where a table stores a single record per row in a standard 1:1 key-value attribute set. This is how data is stored in RDBMS' like Oracle, MySQL and SQL Server. Your second example looks more like a document database or NoSQL database, where data is stored in nested data objects (like hashes and arrays). This is how data is stored in database systems like MongoDB.
There are benefits and costs to storing data in either model. With relational databases, where data is spread accross multiple tables and linked by keys, it is easy to get at data from multiple angles and aggregate it for multiple purposes. With document databases, data is typically more difficult to join in single queries, but much faster to retrieve, and also typically formatted for quicker application use.
For your application, the latter (document database model) might be best if you only care about referencing a user's images when you have a user ID. This would not be ideal for say, querying for all images of category 'profile pic' or for all images uploaded after a certain date. You could probably accomplish your task with either database type, and choosing the right database will always depend on the application(s) that it will be used for, but as a general rule-of-thumb, relational databases are more flexible and hard to go wrong with.
What you want (having user -> (photo1, photo2, ...)) is kind of an INDEX :
When you execute your request, it will go to the INDEX and fetch the INDEX "user" in the photos table, and get the photo list to fetch. Not all the database will be looked up, it's optimised.
I would do something like
Users_Table(One User - One Photo)
With all the column that every user will have. if one user will have only one photo then just add a column in this table with photo_url
One User Many Photos
If one User Can have multiple Photos. then create a table separately for photos which contains only UserID from Users_Table and the Photo_ID and Photo_File.
Many Users Many Photos
If One Photo can be assigned to multiple users then Create a Separate table for Photos Where there are PhotoID and Photo_File. Third Table User_Photos which can have UserID from Users_Table and Photo_ID from Photos Table.
EDIT:
Would it be a good idea to just keep it all under 1 big table and have a flag that differentiates the different forms?
I have to build a site with 5 forms, maybe more. so far the fields for the forms are the following:
What would be the best approach to normalize this design?
I was thinking about splitting "Personal Details" into 3 different tables:
and then reference them from the others with an ID...
Would that make sense? It looks like I'll end up with lots of relationships...
Normalized data essentially means that the same data is not stored multiple times in multiple places. For example, instead of storing the customer contact info with an order, the customer ID is stored with the order and the customer's contact information is 'related' to the order. When the customer's phone number is updated, there is only one place the phone number needs to be updated (the customer table) and all the orders will have the correct information without being updated. Each piece of data exists in one, and only one, place. This is normalized data.
So, to answer your question: no, you will not make your database structure more normalized by breaking up a large table as you described.
The reason to break up a single table into multiple tables is usually to create a one to many relationship. For example, one person might have multiple e-mail addresses. Or multiple physical addresses. Another common reason for breaking up tables is to make systems modular, so that tables can be created that join to existing tables without modifying the existing tables.
Breaking one big table into multiple little tables, with a one to one relationship between them, doesn't make the data any more normalized, it just makes your queries more of a pain to write.* And you don't want to structure your database design around interfaces (forms) unless there is a good reason. There usually isn't.
*Although there are sometimes good reasons to break up big tables and create one to one relationships, normalization isn't one of them.
I understand the concept of database normalization, but always have a hard time explaining it in plain English - especially for a job interview. I have read the wikipedia post, but still find it hard to explain the concept to non-developers. "Design a database in a way not to get duplicated data" is the first thing that comes to mind.
Does anyone has a nice way to explain the concept of database normalization in plain English? And what are some nice examples to show the differences between first, second and third normal forms?
Say you go to a job interview and the person asks: Explain the concept of normalization and how would go about designing a normalized database.
What key points are the interviewers looking for?
Well, if I had to explain it to my wife it would have been something like that:
The main idea is to avoid duplication of large data.
Let's take a look at a list of people and the country they came from. Instead of holding the name of the country which can be as long as "Bosnia & Herzegovina" for every person, we simply hold a number that references a table of countries. So instead of holding 100 "Bosnia & Herzegovina"s, we hold 100 #45. Now in the future, as often happens with Balkan countries, they split to two countries: Bosnia and Herzegovina, I will have to change it only in one place. well, sort of.
Now, to explain 2NF, I would have changed the example, and let's assume that we hold the list of countries every person visited.
Instead of holding a table like:
Person CountryVisited AnotherInformation D.O.B.
Faruz USA Blah Blah 1/1/2000
Faruz Canada Blah Blah 1/1/2000
I would have created three tables, one table with the list of countries, one table with the list of persons and another table to connect them both. That gives me the most freedom I can get changing person's information or country information. This enables me to "remove duplicate rows" as normalization expects.
One-to-many relationships should be represented as two separate tables connected by a foreign key. If you try to shove a logical one-to-many relationship into a single table, then you are violating normalization which leads to dangerous problems.
Say you have a database of your friends and their cats. Since a person may have more than one cat, we have a one-to-many relationship between persons and cats. This calls for two tables:
Friends
Id | Name | Address
-------------------------
1 | John | The Road 1
2 | Bob | The Belltower
Cats
Id | Name | OwnerId
---------------------
1 | Kitty | 1
2 | Edgar | 2
3 | Howard | 2
(Cats.OwnerId is a foreign key to Friends.Id)
The above design is fully normalized and conforms to all known normalization levels.
But say I had tried to represent the above information in a single table like this:
Friends and cats
Id | Name | Address | CatName
-----------------------------------
1 | John | The Road 1 | Kitty
2 | Bob | The Belltower | Edgar
3 | Bob | The Belltower | Howard
(This is the kind of design I might have made if I was used to Excel-sheets but not relational databases.)
A single-table approach forces me to repeat some information if I want the data to be consistent. The problem with this design is that some facts, like the information that Bob's address is "The belltower" is repeated twice, which is redundant, and makes it difficult to query and change data and (the worst) possible to introduce logical inconsistencies.
Eg. if Bob moves I have to make sure I change the address in both rows. If Bob gets another cat, I have to be sure to repeat the name and address exactly as typed in the other two rows. E.g. if I make a typo in Bob's address in one of the rows, suddenly the database has inconsistent information about where Bob lives. The un-normalized database cannot prevent the introduction of inconsistent and self-contradictory data, and hence the database is not reliable. This is clearly not acceptable.
Normalization cannot prevent you from entering wrong data. What normalization prevents is the possibility of inconsistent data.
It is important to note that normalization depends on business decisions. If you have a customer database, and you decide to only record a single address per customer, then the table design (#CustomerID, CustomerName, CustomerAddress) is fine. If however you decide that you allow each customer to register more than one address, then the same table design is not normalized, because you now have a one-to-many relationship between customer and address. Therefore you cannot just look at a database to determine if it is normalized, you have to understand the business model behind the database.
This is what I ask interviewees:
Why don't we use a single table for an application instead of using multiple tables ?
The answer is ofcourse normalization. As already said, its to avoid redundancy and there by update anomalies.
This is not a thorough explanation, but one goal of normalization is to allow for growth without awkwardness.
For example, if you've got a user table, and every user is going to have one and only one phone number, it's fine to have a phonenumber column in that table.
However, if each user is going to have a variable number of phone numbers, it would be awkward to have columns like phonenumber1, phonenumber2, etc. This is for two reasons:
If your columns go up to phonenumber3 and someone needs to add a fourth number, you have to add a column to the table.
For all the users with fewer than 3 phone numbers, there are empty columns on their rows.
Instead, you'd want to have a phonenumber table, where each row contains a phone number and a foreign key reference to which row in the user table it belongs to. No blank columns are needed, and each user can have as few or many phone numbers as necessary.
One side point to note about normalization: A fully normalized database is space efficient, but is not necessarily the most time efficient arrangement of data depending on use patterns.
Skipping around to multiple tables to look up all the pieces of info from their denormalized locations takes time. In high load situations (millions of rows per second flying around, thousands of concurrent clients, like say credit card transaction processing) where time is more valuable than storage space, appropriately denormalized tables can give better response times than fully normalized tables.
For more info on this, look for SQL books written by Ken Henderson.
I would say that normalization is like keeping notes to do things efficiently, so to speak:
If you had a note that said you had to
go shopping for ice cream without
normalization, you would then have
another note, saying you have to go
shopping for ice cream, just one in
each pocket.
Now, In real life, you would never do
this, so why do it in a database?
For the designing and implementing part, thats when you can move back to "the lingo" and keep it away from layman terms, but I suppose you could simplify. You would say what you needed to at first, and then when normalization comes into it, you say you'll make sure of the following:
There must be no repeating groups of information within a table
No table should contain data that is not functionally dependent on that tables primary key
For 3NF I like Bill Kent's take on it: Every non-key attribute must provide a fact about the key, the whole key, and nothing but the key.
I think it may be more impressive if you speak of denormalization as well, and the fact that you cannot always have the best structure AND be in normal forms.
Normalization is a set of rules that used to design tables that connected through relationships.
It helps in avoiding repetitive entries, reducing required storage space, preventing the need to restructure existing tables to accommodate new data, increasing speed of queries.
First Normal Form: Data should be broken up in the smallest units. Tables should not contain repetitive groups of columns. Each row is identified with one or more primary key.
For example, There is a column named 'Name' in a 'Custom' table, it should be broken to 'First Name' and 'Last Name'. Also, 'Custom' should have a column named 'CustiomID' to identify a particular custom.
Second Normal Form: Each non-key column should be directly related to the entire primary key.
For example, if a 'Custom' table has a column named 'City', the city should has a separate table with primary key and city name defined, in the 'Custom' table, replace the 'City' column with 'CityID' and make 'CityID' the foreign key in the tale.
Third normal form: Each non-key column should not depend on other non-key columns.
For example, In an order table, the column 'Total' is dependent on 'Unit price' and 'quantity', so the 'Total' column should be removed.
I teach normalization in my Access courses and break it down a few ways.
After discussing the precursors to storyboarding or planning out the database, I then delve into normalization. I explain the rules like this:
Each field should contain the smallest meaningful value:
I write a name field on the board and then place a first name and last name in it like Bill Lumbergh. We then query the students and ask them what we will have problems with, when the first name and last name are all in one field. I use my name as an example, which is Jim Richards. If the students do not lead me down the road, then I yank their hand and take them with me. :) I tell them that my name is a tough name for some, because I have what some people would consider 2 first names and some people call me Richard. If you were trying to search for my last name then it is going to be harder for a normal person (without wildcards), because my last name is buried at the end of the field. I also tell them that they will have problems with easily sorting the field by last name, because again my last name is buried at the end.
I then let them know that meaningful is based upon the audience who is going to be using the database as well. We, at our job will not need a separate field for apartment or suite number if we are storing people's addresses, but shipping companies like UPS or FEDEX might need it separated out to easily pull up the apartment or suite of where they need to go when they are on the road and running from delivery to delivery. So it is not meaningful to us, but it is definitely meaningful to them.
Avoiding Blanks:
I use an analogy to explain to them why they should avoid blanks. I tell them that Access and most databases do not store blanks like Excel does. Excel does not care if you have nothing typed out in the cell and will not increase the file size, but Access will reserve that space until that point in time that you will actually use the field. So even if it is blank, then it will still be using up space and explain to them that it also slows their searches down as well.
The analogy I use is empty shoe boxes in the closet. If you have shoe boxes in the closet and you are looking for a pair of shoes, you will need to open up and look in each of the boxes for a pair of shoes. If there are empty shoe boxes, then you are just wasting space in the closet and also wasting time when you need to look through them for that certain pair of shoes.
Avoiding redundancy in data:
I show them a table that has lots of repeated values for customer information and then tell them that we want to avoid duplicates, because I have sausage fingers and will mistype in values if I have to type in the same thing over and over again. This “fat-fingering” of data will lead to my queries not finding the correct data. We instead, will break the data out into a separate table and create a relationship using a primary and foreign key field. This way we are saving space because we are not typing the customer's name, address, etc multiple times and instead are just using the customer's ID number in a field for the customer. We then will discuss drop-down lists/combo boxes/lookup lists or whatever else Microsoft wants to name them later on. :) You as a user will not want to look up and type out the customer's number each time in that customer field, so we will setup a drop-down list that will give you a list of customer, where you can select their name and it will fill in the customer’s ID for you. This will be a 1-to-many relationship, whereas 1 customer will have many different orders.
Avoiding repeated groups of fields:
I demonstrate this when talking about many-to-many relationships. First, I draw 2 tables, 1 that will hold employee information and 1 that will hold project information. The tables are laid similar to this.
(Table1)
tblEmployees
* EmployeeID
First
Last
(Other Fields)….
Project1
Project2
Project3
Etc.
**********************************
(Table2)
tblProjects
* ProjectNum
ProjectName
StartDate
EndDate
…..
I explain to them that this would not be a good way of establishing a relationship between an employee and all of the projects that they work on. First, if we have a new employee, then they will not have any projects, so we will be wasting all of those fields, second if an employee has been here a long time then they might have worked on 300 projects, so we would have to include 300 project fields. Those people that are new and only have 1 project will have 299 wasted project fields. This design is also flawed because I will have to search in each of the project fields to find all of the people that have worked on a certain project, because that project number could be in any of the project fields.
I covered a fair amount of the basic concepts. Let me know if you have other questions or need help with clarfication/ breaking it down in plain English. The wiki page did not read as plain English and might be daunting for some.
I've read the wiki links on normalization many times but I have found a better overview of normalization from this article. It is a simple easy to understand explanation of normalization up to fourth normal form. Give it a read!
Preview:
What is Normalization?
Normalization is the process of
efficiently organizing data in a
database. There are two goals of the
normalization process: eliminating
redundant data (for example, storing
the same data in more than one table)
and ensuring data dependencies make
sense (only storing related data in a
table). Both of these are worthy goals
as they reduce the amount of space a
database consumes and ensure that data
is logically stored.
http://databases.about.com/od/specificproducts/a/normalization.htm
Database normalization is a formal process of designing your database to eliminate redundant data. The design consists of:
planning what information the database will store
outlining what information users will request from it
documenting the assumptions for review
Use a data-dictionary or some other metadata representation to verify the design.
The biggest problem with normalization is that you end up with multiple tables representing what is conceptually a single item, such as a user profile. Don't worry about normalizing data in table that will have records inserted but not updated, such as history logs or financial transactions.
References
When not to Normalize your SQL Database
Database Design Basics
+1 for the analogy of talking to your wife. I find talking to anyone without a tech mind needs some ease into this type of conversation.
but...
To add to this conversation, there is the other side of the coin (which can be important when in an interview).
When normalizing, you have to watch how the databases are indexed and how the queries are written.
When in a truly normalized database, I have found that in situations it's been easier to write queries that are slow because of bad join operations, bad indexing on the tables, and plain bad design on the tables themselves.
Bluntly, it's easier to write bad queries in high level normalized tables.
I think for every application there is a middle ground. At some point you want the ease of getting everything out a few tables, without having to join to a ton of tables to get one data set.