Below is the discussed lesson on SSAS (SQL Server Analysis Services) basic theory covered while teaching concepts to students during MSBI classroom training in Mumbai.
Normalization design technique not good for OLAP database.
- The reason normalization is not good design technique for OLAP database is that the main purpose of normalization is to remove redundancy or duplication in data.
- It means for insert, update and delete of data normalization is used as there are no duplicate records and will have better performance. But for reading the data star schema/snow flake design is used and has better performance.
- Therefore different other design techniques are used for designing database like Star Schema, Snow Flake, etc.
- These schemas are used because their main purpose is for fast retrieval of data.
Difference between Star Schema & Snow Flake Schema.
- Star Schema: In star schema there is one Fact or Measure and there are many Dimensions which are connected to that Fact or Measures.
- Snow Flake Schema: In snow flake schema the dimensions are connected to the Measure or Fact as the regular star schema but here in snow flake apart from connecting to the Fact the dimensions are also connected with other Dimensions. This is what makes a difference between snow flake and star schema.
Defining Measure & Dimension.
- Measure: Measures are the numeric or those values which helps us to analyze data. The measures usually have the Master data due to which it makes it easier to analyze the data. So analysis or forecasting of data is done by using numbers so measures have numeric values.
- Dimension: They are nothing but context to the numbers or the numeric data. It is defined that the context that describe more about numbers (Measures) is a dimension.
There are many essential fundamental topic and some fundamentals comes across while doing practical lab during training we try to cover as maximum we can. Every hour spent during classroom MSBI training in Mumbai is worth to attend.
