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Compare SQL changes | SQL Database Modeler

Effortlessly compare SQL changes and optimize your database structure with SQL Database Modeler u2013 your go-to solution for streamlined database design and management.

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Compare SQL changes | SQL Database Modeler

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  1. Introduction to SQL Server Dimensional Modeling SQL Server Dimensional Modeling is a data warehousing technique used to organize and analyze complex business data. It focuses on creating a logical data structure that enables efficient reporting and decision-making.

  2. Fact Tables and Dimension Tables Fact Tables Dimension Tables Relationship Fact tables contain the core business metrics or measures, such as sales, revenue, or production data. Dimension tables provide contextual information about the facts, such as product details, customer information, or time periods. Fact tables link to dimension tables through foreign key relationships, forming a dimensional data model.

  3. Star Schema and Snowflake Schema 1 Star Schema A simple dimensional model with a central fact table surrounded by dimension tables. 2 Snowflake Schema A more complex model where dimension tables are further normalized into additional tables. 3 Tradeoffs Star schema is easier to understand and query, while snowflake schema can be more efficient for certain use cases.

  4. Slowly Changing Dimensions 1 2 Type 1 Type 2 Overwrite existing data, no history kept. Add new rows to track historical changes. 3 4 Type 3 Type 4 Add additional columns to store previous values. Maintain a mini-dimension to store historical changes.

  5. Degenerate Dimensions What are they? Why use them? Degenerate dimensions are dimensions that exist within the fact table itself, rather than as a separate dimension table. Degenerate dimensions can simplify the data model and improve query performance by reducing the number of joins. Examples Considerations Common examples include invoice numbers, order numbers, and batch IDs. Carefully evaluate when to use degenerate dimensions to maintain a balance between simplicity and flexibility.

  6. Junk Dimensions Miscellaneous Efficient Flexible Simplify Junk dimensions store low-cardinality attributes that don't fit well into other dimensions. Junk dimensions can improve query performance by consolidating these small attributes. Junk dimensions allow the data model to evolve without significantly changing the fact table structure. Junk dimensions help keep the data model clean and organized.

  7. Aggregate Fact Tables Raw Data Fact tables contain detailed, granular business data. Aggregation Aggregate fact tables pre-calculate and store summarized data. Performance Aggregate fact tables enable faster reporting and analytics on large datasets.

  8. Best Practices for Dimensional Modeling 1 2 Start with the Business Keep it Simple Understand the business requirements and user needs before designing the data model. Strive for a clean, intuitive data structure that is easy to understand and use. 3 4 Be Flexible Focus on Performance Design the model to accommodate future growth and changes in the business. Optimize the model for efficient querying and analysis, using techniques like aggregation. https://sqldbm.com/

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