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I am involved in a new project where I have to create a data cube from the existing relational database system. I understood that the existing system is not properly designed, and I am not sure where to start.
My question are:
After spending some time, reading reference books, I came to the point, where I could define the difference between the star schema and data cubes. I could not comment on this definition but this answer satisfies me and help me to start the task. On the process, I hope I get better understanding (if exists) of these techniques. Here is my findings:
Difference between Star Schema and data cubes:
Star schema is a dimensional modeling technique. It contains, Dimensions and Facts (business measurements). Mostly used in Data warehouse technology.
Data cube is a multi-dimensional table. It means, combination of dimension and fact tables. Mostly used in OLAP analysis tools.
Data cubes are built on the star schema to improve the query performance - performing aggregate and summarizing measurements.
It will pre-calculate the values, instead of calculating on the fly, so it boosts performance.
Example: Total item, Sales Amount, etc
Where I have to start?
I realized I have to start from, star schema and build data cubes on it. Data cubes are not built on the DBMS system, but outside the DBMS system as aggregates and other operations.
I hope this answer will help the people who are new to this technologies. If I am missing something or understanding wrongly, please correct it. Thank you.
Qutoting from Kimball Dimensional Modeling Techniques
Star Schemas and OLAP cubes
Star schemas are dimensional structures deployed in a relational database management system (RDBMS). They characteristically consist of fact tables linked to associated dimension tables via primary/foreign key relationships. An online analytical processing (OLAP) cube is a dimensional structure implemented in a multidimensional database; it can be equivalent in content to, or more often derived from, a relational star schema. An OLAP cube contains dimensional attributes and facts, but it is accessed through languages with more analytic capabilities than SQL, such as XMLA. OLAP cubes are included in this list of basic techniques because an OLAP cube is often the final step in the deployment of a dimensional DW/BI system, or may exist as an aggregate structure based on a more atomic relational star schema.
