Data integration basics-Dimension Confirmation.

The purpose of this topic is to establish the basics for design of ETL processes. With out the understanding an ETL process for data integration cannot be designed or developed. 

A database can have multiple sources. Multiple sources may contain a data set of entirely different subject areas, but some data set will intersect. Example Sales data and salary data will have employee as the common set. 

Data Set Intersection.JPG 

Between two or more sources, the subjects, entities or even attributes can be common. So can we integrate the data easily? Mathematically it seems very easy, but the real world is not just about numbers or exact same string values. Every thing can be similar or same but may be not represented in the exact same manner in all the sources. The differences in representation of same information and facts between two or more sources can create some of the most interesting challenges in Data Integration.

Data integration: -The first step in data integration is identification of common elements
1. Identify the common entities.
Example, Employee dimension can come from Sales system, Payroll System, etc. Products can come from manufacturing, sales, purchase etc. Once the common entity is identified its definition should be standardized. Example, does employee include fulltime employees as well as temporary workers?
 
2. Identify the common attributes.
What are the attributes that are common to employee, 1st name, 2nd name, last name, date of joining, etc? Each attribute should be defined.

3. Identify the common values
Same information can be represented in different forms in multiple source system. Example, male sex, can be represented as ‘M’ or ‘1′ or ‘male’ or some thing else by each source system. A common representation must be decided (example, ‘Male’). Also if necessary a finite set of values should be established. Example the employee sex = (‘Male’,’ Female’) and it will not allow more than these two values.

The second step is the identification of Data Steward who will own the responsibility and ownership for particular set data elements.

The third step is to design an ETL process to integrate the data into the target.  This is the most important area in the implementation of an ETL process for data integration. This topic will be discussed in more detailed under its own heading.

The final fourth step is to establish a process of maintenance, review & reporting of such elements.


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