Data Cubes:
- Definition: A data cube is a multi-dimensional array of values, typically used to represent data along multiple dimensions for complex querying and analysis.
- Purpose: Facilitates efficient querying and analysis by pre-aggregating data along various dimensions.
- Structure: Comprised of cells, each representing a specific combination of dimension values and containing a measure or set of measures.
- Operations:
- Slicing: Extracting a single layer (or slice) of the cube for a particular dimension value.
- Dicing: Creating a sub-cube by selecting specific values across multiple dimensions.
- Drill-Down/Drill-Up: Navigating between levels of detail in the hierarchy of dimensions.
- Pivoting (Rotating): Changing the orientation of the cube to view data from different perspectives.
Star Schema
Star Schema:
- Definition: A type of database schema that organizes data into fact and dimension tables, resembling a star shape with the fact table at the center.
- Structure:
- Fact Table: Central table containing quantitative data (facts) and foreign keys linking to dimension tables.
- Dimension Tables: Surrounding tables containing descriptive attributes (dimensions) related to the facts.
- Advantages:
- Simple and easy to understand.
- Efficient for querying due to denormalized dimension tables, which reduces the need for joins.
- Example:
- Sales Fact Table: Columns include DateKey, ProductKey, StoreKey, SalesAmount, QuantitySold.
- Time Dimension: Columns include DateKey, Year, Quarter, Month, Day.
- Product Dimension: Columns include ProductKey, Category, Subcategory, ProductName.
- Store Dimension: Columns include StoreKey, StoreName, Location.
Snowflake Schema
Snowflake Schema:
- Definition: An extension of the star schema where dimension tables are normalized into multiple related tables, forming a snowflake-like structure.
- Structure:
- Fact Table: Same as in the star schema, central and connected to dimension tables.
- Normalized Dimension Tables: Dimension tables are further split into related tables to eliminate redundancy.
- Advantages:
- Reduces data redundancy and storage requirements.
- Maintains data integrity by organizing data into related tables.
- Disadvantages:
- More complex queries due to multiple joins.
- Can result in slower query performance compared to the star schema.
- Example:
- Sales Fact Table: As in the star schema.
- Time Dimension: Normalized into separate tables for Year, Quarter, Month, Day.
- Product Dimension: Normalized into separate tables for Category, Subcategory, ProductName.
- Store Dimension: Normalized into separate tables for StoreName and Location.
Fact Constellation Schema (Galaxy Schema)
Fact Constellation Schema (Galaxy Schema):
- Definition: A complex schema that contains multiple fact tables sharing dimension tables, forming a constellation-like structure.
- Structure:
- Multiple Fact Tables: Each fact table may represent different processes or events but share common dimensions.
- Shared Dimension Tables: Dimensions are shared across different fact tables to provide a unified view of the data.
- Advantages:
- Flexible and scalable for complex data warehouses.
- Supports complex business processes and multiple subject areas.
- Disadvantages:
- Increased complexity in schema design and maintenance.
- Can lead to more complex query optimization.
- Example:
- Sales Fact Table: Contains measures related to sales transactions.
- Orders Fact Table: Contains measures related to order processing.
- Shared Dimensions: Time, Product, Store dimensions used by both fact tables.
Conceptual Summary
- Data Cubes: Enable multi-dimensional analysis by organizing data into a cube structure that supports efficient querying and analytical operations.
- Star Schema: A simple and efficient schema design with a central fact table and surrounding denormalized dimension tables, ideal for straightforward querying.
- Snowflake Schema: A normalized version of the star schema that reduces data redundancy at the cost of more complex queries and potentially slower performance.
- Fact Constellation Schema: A more complex schema that supports multiple fact tables and shared dimensions, suitable for large and intricate data warehouses with diverse analytical needs.
Visual Examples
- Star Schema:
sql
+-----------------+| Time Dim |
+-----------------+
|
|
+-----------------+-----------------+
| Fact Table (Sales) |
+-----------------+-----------------+
|
|
+-----------------+-----------------+
| Product Dim | Store Dim |
+-----------------+-----------------+
- Snowflake Schema:
sql
+-----------------+| Year Dim |
+-----------------+
|
|
+-----------------+
| Quarter Dim |
+-----------------+
|
|
+-----------------+
| Month Dim |
+-----------------+
|
|
+-----------------+-----------------+
| Fact Table (Sales) |
+-----------------+-----------------+
|
|
+-----------------+-----------------+
| Category Dim | Store Dim |
+-----------------+-----------------+
|
|
+-----------------+
| Subcategory Dim |
+-----------------+
- Fact Constellation Schema:
sql
+-----------------+| Time Dim |
+-----------------+
/ \
/ \
/ \
+-----------------+ +-----------------+
| Sales Fact Table| | Orders Fact Table|
+-----------------+ +-----------------+
/ \ / \
/ \ / \
+—————–+ +—————–+ | Product Dim | | Store Dim | +—————–+ +—————–+
sql
 the multi-dimensional data model is crucial for designing data warehouses