MSDN Community

Data Modeling Best Practices for Analysis Services

Effective data modeling is crucial for building performant and maintainable Analysis Services solutions. This guide outlines key best practices to ensure your models are robust, scalable, and easy to understand.

1. Understand Your Business Requirements

Before you start modeling, thoroughly understand the business questions your solution needs to answer. This will guide your schema design and ensure you capture the necessary data relationships and hierarchies.

2. Star Schema Design

Employ a star schema design where possible. This involves:

• Fact Tables: Contain quantitative measures (e.g., sales amount, quantity) and foreign keys referencing dimension tables. Keep fact tables narrow and deep.
• Dimension Tables: Contain descriptive attributes that provide context to the facts (e.g., product name, customer city, date). Dimension tables should be denormalized to avoid complex joins.

3. Naming Conventions

Consistent and clear naming conventions are essential for model understandability. Use:

• Descriptive names for tables, columns, and measures.
• Avoid special characters and spaces where possible, or use appropriate quoting mechanisms.
• Use singular nouns for dimension tables and plural nouns for fact tables.

Example:

-- Good
DIM_Customer
DIM_Product
FACT_Sales

-- Avoid
CustomersTbl
Products_Table
SalesFact
            

4. Data Types and Precision

Choose appropriate data types for columns to optimize storage and performance. Ensure correct precision for numerical data, especially for monetary values.

• Use `INT` for whole numbers.
• Use `DECIMAL` or `NUMERIC` for financial data where exact precision is critical.
• Use `DATE`, `DATETIME`, or `DATETIME2` for temporal data.

5. Handling Hierarchies

Implement natural hierarchies within dimensions (e.g., Date: Year -> Quarter -> Month -> Day; Geography: Country -> State -> City). Analysis Services provides robust features for defining these hierarchies, which greatly enhance user navigation and analysis.

6. Measures and Aggregations

Define measures clearly and efficiently. Consider pre-aggregating data where appropriate for performance-critical scenarios, although Analysis Services' in-memory capabilities often mitigate the need for extensive pre-aggregation.

• Use standard aggregation functions like `SUM`, `COUNT`, `AVERAGE`, `MIN`, `MAX`.
• For complex calculations, consider using DAX (Data Analysis Expressions).

7. Data Granularity

Determine the appropriate granularity for your fact tables. This is the lowest level of detail at which your data will be stored. Ensure it supports your business requirements without being excessively granular, which can lead to performance issues.

8. Performance Considerations

Regularly review and tune your model for performance. This includes:

• Optimizing DAX queries.
• Partitioning large tables.
• Leveraging columnar storage and compression.
• Monitoring query performance metrics.

9. Documentation

Document your data model thoroughly. This includes explaining the purpose of each table, the relationships between them, the definition of key measures, and any specific design decisions made.

By adhering to these best practices, you can build powerful and efficient Analysis Services solutions that provide valuable insights to your users.