How do you deal with changing business requirements and data updates in star and snowflake schemas?

1 What are star and snowflake schemas?

Star and snowflake schemas are types of dimensional models, which organize data into facts and dimensions. Facts are numerical measures of business events, such as sales, orders, or transactions. Dimensions are descriptive attributes that provide context and meaning to the facts, such as date, customer, product, or location. A star schema consists of a central fact table that connects to multiple dimension tables, forming a star-like shape. A snowflake schema is a variation of the star schema, where some dimension tables are normalized into sub-dimension tables, forming a snowflake-like shape.

2 Why do business requirements and data updates change?

Business requirements and data updates change for various reasons, such as market trends, customer feedback, regulatory compliance, data quality, or performance optimization. For example, a business may want to add new dimensions or facts to support new analytical queries, or modify existing ones to reflect changes in business logic or definitions. Alternatively, a data source may provide new or updated data that needs to be integrated into the existing schema, or correct errors or inconsistencies that affect the accuracy or completeness of the data.

3 How to deal with changing business requirements in star and snowflake schemas?

Changing business requirements in star and snowflake schemas can be handled by applying different types of schema changes, depending on the nature and impact of the change. Schema changes can be classified into three categories: additive, subtractive, and structural. Additive changes involve adding new facts or dimensions to the schema, without affecting the existing ones. Subtractive changes involve removing facts or dimensions from the schema, which may affect the existing ones. Structural changes involve modifying the structure or relationships of the facts or dimensions in the schema, which may affect the existing ones. Each type of schema change has different implications for the complexity, flexibility, and compatibility of the schema.

4 How to deal with data updates in star and snowflake schemas?

Data updates in star and snowflake schemas can be handled by applying different types of data loading techniques, depending on the frequency and volume of the updates. Data loading techniques can be classified into two categories: full load and incremental load. Full load involves replacing the entire data in the schema with the latest data from the source, which ensures data consistency and accuracy, but may be time-consuming and resource-intensive. Incremental load involves updating only the data that has changed since the last load, which reduces the loading time and resources, but may introduce data inconsistency or duplication.

5 What are the advantages and disadvantages of star and snowflake schemas?

Star and snowflake schemas have different advantages and disadvantages, depending on the trade-offs between simplicity, performance, and scalability. Star schemas are simpler and faster to query, as they have fewer joins and less redundancy. However, they may require more storage space and maintenance, as they have more denormalized data and duplication. Snowflake schemas are more normalized and efficient to store, as they have less redundancy and duplication. However, they may be more complex and slower to query, as they have more joins and levels. The choice between star and snowflake schemas depends on the characteristics and requirements of the data and the analysis.

6 Here’s what else to consider

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