Online GUID Generator's

http://www.guidgenerator.com/online-guid-generator.aspx
http://www.somacon.com/p113.php
http://www.hoskinson.net/GuidGenerator/default.asp
http://www.fileformat.info/tool/guid.htm
https://developer.mozilla.org/en/Generating_GUIDs

Oracle's GUID

SYS_GUID generates and returns a globally unique identifier (RAW value) made up of 16 bytes. On most platforms, the generated identifier consists of a host identifier, a process or thread identifier of the process or thread invoking the function, and a nonrepeating value (sequence of bytes) for that process or thread.

Examples

The following example adds a column to the sample table hr.locations, inserts unique identifiers into each row, and returns the 32-character hexadecimal representation of the 16-byte RAW value of the global unique identifier:

Example 1:

ALTER TABLE locations ADD (uid_col RAW(16));

UPDATE locations SET uid_col = SYS_GUID();

SELECT location_id, uid_col FROM locations
ORDER BY location_id, uid_col;

LOCATION_ID UID_COL
----------- ----------------------------------------------------------------
1000 09F686761827CF8AE040578CB20B7491
1100 09F686761828CF8AE040578CB20B7491
1200 09F686761829CF8AE040578CB20B7491
1300 09F68676182ACF8AE040578CB20B7491
1400 09F68676182BCF8AE040578CB20B7491
1500 09F68676182CCF8AE040578CB20B7491

Example 2:

create table test (ID raw(16) default sys_guid(), name varchar2(10));

insert into test(name) values ('DAS');

insert into test(name) values ('DDAS');

select * from test;

ID NAME
-------------------------------- ----------
CF874CB4F32A47C19859DA2BB0D71AB4 DAS
2B768E600A484391B6B8D5C4924D2785 DDAS

Facts and Dimensions

The data in a data warehouse is divided into "facts" and "dimensions". Facts are tangible events which also carry inherent characteristics. Dimensions are any data elements that may affect the behavior of these facts.

- At the center of the star are the facts. Facts are tangible events. In this case, the facts are individual sales transactions.
- Around the facts are five dimensions:
1) Customer Loyalty Dimension
2) Geographic Dimension
3) Product Dimension
4) HR Dimension
5) Time Dimension

- Further defining the Geographic dimension are two sub-dimensions, also known as “snowflake” dimensions because of the shape they give to the star:
1) Tax Snowflake Dimension, which depends on the geographic location and the time when the fact occurred
2) Weather Snowflake Dimension, which also depends on the geographic location and the time when the fact occurred

Having divided the data into facts and dimensions, one can mine the data for trends. In a retail environment, one could look for questions such as:
• What distance will the average loyalty card holding customer travel from their home to one of the company retail stores?
• Is there a correlation between the distance and the frequency of the visits?
• If a promotion flyer was distributed by mail to a given postal code, what was the loyalty card holder response?
• In the spring season, at what average temperature do customers purchase more cold drinks, like fruit juices than hot drinks, like coffee?
• If a customer bought a product in the “salty snack” category, what is the probability that they would also buy one or more cold drinks?
• Is there a typical “basket of goods” purchased on certain weekdays?
• What is the profile of the employees with the best sales?
• If a sales education course was provided for employees of a given territory, can the results be measured?

Beyond having answers to questions that the marketers may be curious about, the secondary aim of the data warehouse star schema is to enable “data mining”. Effectively, data mining is the use of software to uncover hitherto unknown trends, or trends not easily visible otherwise.

Data Warehouse verses Data Mart

Definitely spend time upfront thinking about what you're trying to achieve, not just today's requirements.

A traditional data warehouse is all-about providing a vehicle for reporting from summary and aggregate information (using de-normalized tables, summarized tables and materialized views). Most data warehouse designers replicate the data warehouse summary data onto another instance to avoid contention with the OLTP database, but this depends on the traffic on your system and the ability of your server to handle additional load.

A data warehouse pre-summarizes and pre-aggregates the OLTP data so that the queries can fetch the result sets with only a few data block touches. Make sure that your OLTP server has enough CPU resources to support Oracle parallel query, as you will need it to roll-up your summaries and aggregates

If your existing summary tables do not require joins into other OLTP tables, then you will not benefit from a star transformation approach.

The bottom-up approach to data warehouse design. In this approach, data marts, or "mini data warehouse" data storage facilities are first created to provide reporting and analytical capabilities for specific business processes. Like bricks forming a wall, the data contained within these data marts can eventually be combined to create a more comprehensive data warehouse.

The top-down design method, as it involves a lot of up-front end-in-mind planning before any results can be extracted.