Differentiate between star and snowflake schema and different filters levels ? Power BI interview questions and answers 380

 Differentiate between star and snowflake schema and different filters levels ?

Here's a comprehensive comparison of star and snowflake schemas, along with different filter levels in data modeling:

Schema Structure:

Star Schema:

• Simpler structure: Consists of one central fact table surrounded by multiple dimension tables.

• Direct relationships: Dimension tables connect directly to the fact table using foreign keys.

• More denormalized: Dimensions may contain some redundancy for faster query performance.

Snowflake Schema:

• More complex structure: Extends the star schema by normalizing dimension tables further.

• Hierarchical relationships: Dimension tables can have sub-dimensions, creating a multi-level hierarchy.

• More normalized: Reduces redundancy and storage space, but can impact query performance.

Filter Levels:

• Visual-level filters: Applied directly to individual visuals, affecting only that specific visualization.

• Page-level filters: Applied to all visuals on a report page, ensuring consistency across multiple views.

• Report-level filters: Applied to all visuals throughout an entire report, providing a global filtering context.

• Data model filters: Applied at the data source or model level, affecting all downstream visuals and calculations.

Choosing the Right Schema:

• Star schema: Well-suited for simpler data models, faster query performance, and ease of understanding.

• Snowflake schema: Preferred for complex data models, normalization benefits, and reduced storage requirements.

Factors to Consider:

• Data complexity: Evaluate the number of dimensions and relationships within your data.

• Query performance: Consider the speed and efficiency needed for data retrieval.

• Data storage: Assess storage space requirements and costs.

• Data integrity: Determine the importance of data accuracy and consistency.

Additional Tips:

• Hybrid approaches: Combine star and snowflake structures for optimal balance.

• Model optimization: Regularly review and adjust schemas based on changing data and analysis needs.

• Filter strategies: Employ filter levels strategically for effective data exploration and analysis.

By understanding these concepts, you can make informed decisions about data model design and filter usage to effectively analyze your data and derive meaningful insights.

Feel free to ask if you have any further questions or need specific guidance on choosing the right schema or filter strategy for your particular scenario!