Schemas and Data Modelling in Power BI

Power BI has proven to be a resourceful tool when it comes to shaping data. Large data is made simple and easy to understand.

What is Data Modelling?

Data modelling in Power BI is organizing data and creating relationships between different tables so that data works together correctly.

Importance of data modelling

. It makes data easy to understand and be used.
. Provides accurate results analysis
. Reduces data duplication
. Improves performance
. Scalability - it can handle large volume of data with ease

Levels of Data Abstraction

1. Physical level - How data is stored
2. Logical level - What data is stored
3. View level - What the user sees.

Example of data abstraction in Power Bi:

.Physical - sales table stored in database
.Logical - sales connected to customer tables
.View - sales dashboard showing charts

Power BI data model organization

Schema is how tables are arranged and connected.

Fact table - Contains measurable data or numbers. It enables summarization.
Dimension table - Contains descriptive data. It enables filtering and grouping.

Star Schema

In simple terms, the structure of Star schema is designed to have one central Fact table and multiple Dimension tables. This is the common type.

Advantages of Star Schema

1. Accurate data analysis
2. Easy to understand and use.
3. Improves performance
4. Reduction in data duplicates

Snowflake Schema

Its structure is designed such that the Fact table is connected to Dimension table and those Dimension tables to other Dimension tables.

Advantages of Snowflake Schema

1. Reduces storage space
2. Supports complex queries because of hierarchical data.
3. Useful for large data sets.

Flat Schema

Here, data is stored in one single table without dimensions.

Advantages of Flat Schema

1. Very convenient for small datasets thus easy to set up
2. Easy to understand.

A well designed data model is important for data analysis. It helps in maintaining data integrity. Ensure that you import data, describe relationships and calculations needed on the data before modelling it.
Happy learning and discovery!