Topic: 1. Facts and Dimensions Table 2. Snowflake Schema and Star Schema

<b>ASSIGNMENT</b>Facts and Dimensions TablesIn a data warehouse or business intelligence system, data is typically organized into two main types of tables: fact tables and dimension tables.<b>Fact Tables</b>A fact table is the central table in a data warehouse schema that contains the primary measures or metrics of interest to the business. Fact tables store quantitative data, such as sales amounts, order quantities, or website visits. Each row in a fact table represents a specific business event or transaction, and the columns contain the numeric facts or measures associated with that event. Fact tables typically have a composite primary key made up of the foreign keys that link it to the relevant dimension tables. These foreign keys represent the different dimensions, such as time, product, customer, and location, that provide context for the facts.<b>Advantages of Fact Tables:</b> Enables efficient data aggregation and analysis Supports fast query performance for reporting and analytics Provides a central repository for all key business metrics<b>Disadvantages of Fact Tables:</b> Can become very large, requiring more storage and processing power Requires careful design and modeling to ensure data integrity and consistencyDimension TablesDimension tables contain descriptive information about the various entities or perspectives relevant to the business, such as customers, products, time periods, and locations. Dimension tables provide the context and attributes that allow users to analyze the facts in the fact table from different angles. Dimension tables are typically denormalized, meaning they containredundant data to improve query performance. Each dimension table has a primary key that is used as a foreign key in the fact table.<b>Advantages of Dimension Tables:</b> Provide context and meaning to the facts in the fact table Enable flexible and ad-hoc analysis by allowing users to "slice and dice" the data Improve query performance by reducing the need for complex joinsDisadvantages of Dimension Tables: Can become large and complex as the business grows Require careful management to ensure data consistency and integrity<b>Snowflake Schema and Star Schema</b><b>Snowflake Schema</b>A snowflake schema is a variant of the star schema data model used in data warehousing. In a snowflake schema, the dimension tables are further normalized into multiple related tables, creating a hierarchical or "snowflake" structure. The fact table is still located at the center of the schema, surrounded by the dimension tables. However, each dimension table is broken down into multiple related tables, connected by foreign key relationships.&nbsp;<b>Advantages of Snowflake Schema:</b> Reduced data redundancy and improved data integrity through normalization Easier to maintain and update dimension data More efficient storage utilization<b>Disadvantages of Snowflake Schema:</b> Increased complexity of the schema, which can make it more difficult to understand and query Slower query performance due to the increased number of table joins requiredStar SchemaA star schema is a widely-used data modeling technique in data warehousing that organizes data into a central fact table surrounded by dimension tables. The fact table contains the primary measures or metrics of interest, while the dimension tables provide the contextual information. In a star schema, the dimension tables are denormalized, meaning they contain redundant data to improve query performance. This trade-off between data redundancy and query performance is a key characteristic of the star schema.<b>Advantages of Star Schema:</b> Simpler and more intuitive schema design, making it easier for users to understand and query Faster query performance due to the reduced number of table joins required Easier to implement and maintain<b>Disadvantages of Star Schema:</b> Potential for data redundancy and increased storage requirements Less flexibility in updating dimension data compared to a snowflake schemaSimilarities and DifferencesBoth the snowflake and star schemas are multi-dimensional data models used in data warehousing and business intelligence. They share the following similarities: Centralized fact table surrounded by dimension tables Dimension tables provide context and attributes for the facts in the fact table Designed to support efficient data analysis and reportingThe key difference between the two schemas is the level of normalization of the dimension tables: Snowflake schema: Dimension tables are further normalized into multiple related tables, creating a hierarchical structure. Star schema: Dimension tables are denormalized, containing redundant data to improve query performance.<b>Application</b>Both the snowflake and star schemas are widely used in data warehousing and business intelligence applications. The choice between the two depends on the specific requirements of the project, such as the complexity of the data, the need for data integrity, and the performance requirements of the queries. In general, the star schema is more commonly used due to its simpler design and faster query performance. The snowflake schema may be preferred when data integrity and reduced data redundancy are more important than query performance, or when the data model is highly complex and requires a more normalized structure. Ultimately, the decision to use a snowflake or star schema should be based on a careful analysis of the project requirements, data characteristics, and the trade-offs between schema complexity, data integrity, and query performance.How does the snowflake schema improve query performancethe snowflake schema does not necessarily improve query performance compared to the star schema. In fact, the search results indicate that the star schema is generally faster for querying data due to its denormalized structure and fewer required joins.The key points about how the snowflake schema affects query performance are:<b>Snowflake Schema and Query Performance</b> A snowflake schema has a more normalized and complex structure, where dimension tables are further split into sub-dimension tables. This normalization can make queries more complicated and slower, as there are more tables involved and more joins required. A star schema can be more efficient to query than a snowflake schema, because there are fewer JOINs between tables. The snowflake schema is "more suitable for larger and more complex data sets, where storage space and data quality are more important than query speed and simplicity."So in summary, the snowflake schema does not inherently improve query performance compared to the star schema. In fact, the star schema is generally preferred when query speed and simplicity are more important priorities. The snowflake schema's benefits lie more in reduced data redundancy and improved data integrity, rather than enhanced query performance.