A data architect is designing a data model for a sales data mart. The model features a central fact table containing sales transactions, which is directly linked to dimension tables for products, customers, and store locations. What is the primary advantage of using this type of schema for business intelligence and reporting?
Data storage requirements are minimized by normalizing the dimension tables into multiple related tables.
The schema offers high flexibility by not requiring predefined fields, allowing for the storage of varied data types.
Data integrity is maximized by distributing data across many tables to reduce redundancy, which is ideal for transactional systems.
Query performance is enhanced because the simplified, denormalized structure requires fewer table joins.
The described model is a star schema. Its main advantage is improved query performance for analytical workloads. This is because its denormalized structure requires fewer complex joins to retrieve data compared to a highly normalized (3NF) OLTP schema. While a snowflake schema, which normalizes the dimension tables, can reduce storage space, it introduces more joins and complexity, potentially slowing down queries. A fully normalized schema is optimized for transactional speed and data integrity, not large-scale analytics. An unstructured model would not be suitable for this type of relational, transactional data.
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