Types of Modern Data Warehouse
Types of Modern Data Warehouse
Modern data warehouses employ various data modeling approaches to organize and structure data for efficient storage and analysis. The main types include normalized models, denormalized models (like star schema and snowflake schema), Data Vault 2.0, Kimball’s dimensional modeling, and the One Big Table approach.
Key Characteristics and Data Flow:
- Normalized Data Model: Minimizes redundancy and maintains data integrity
- Bill Inmon’s Approach: Data flows from source systems to 3NF tables, then to data marts
- Denormalized Data Model:
- Kimball’s Dimensional Modeling: Emphasizes business process analysis with shared dimensions
- Data flow: Source systems -> Staging -> Dimension/Fact tables -> OLAP cubes
- Star Schema: Enhances query performance using central fact table linked to dimension tables
- Data flow: Source systems -> ETL -> Fact and Dimension tables -> Reporting tools
- Snowflake Schema: Extends star schema with normalized dimension tables
- Data flow: Source systems -> ETL -> Fact and hierarchical Dimension tables -> Analysis tools
- Kimball’s Dimensional Modeling: Emphasizes business process analysis with shared dimensions
- Data Vault 2.0: Adaptable model for managing historical data and temporal changes
- Data flow: Source systems -> Raw Vault (Hubs, Links, Satellites) -> Business Vault -> Information Mart
- One Big Table: Consolidates all data into a single, expansive table structure
- Data flow: Source systems -> ETL/ELT -> Wide denormalized table -> Direct querying or data marts
Each type has its own advantages and use cases, depending on the specific requirements of the data warehouse and the organization’s needs.