The process of organizing data to minimize redundancy and dependency

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Data Warehouse Structure A data warehouse is a centralized repository of integrated data used for decision support and analysis. Its structure is designed to facilitate efficient querying and reporting. Here are the key components and their roles: 1. Metadata Layer Purpose: Stores information about data elements, their relationships, and data quality. Components: Data dictionary: Defines data elements, their attributes, and relationships. Data quality rules: Defines standards for data accuracy, completeness, and consistency. Business rules: Defines constraints and guidelines for data usage. 2. Staging Area Purpose: Temporarily stores raw data extracted from various source systems.
Components: Extraction, Transformation, and Load (ETL) processes: Extract data from source systems, transform it into a consistent format, and load it into the data warehouse. 3. Data Mart Purpose: Specialized subsets of the data warehouse tailored to specific business needs. Components: Dimension tables: Store descriptive information about data, such as time, product, customer, and location. Fact tables: Phone Number Store numerical measurements or metrics associated with the dimensions. 4. Data Warehouse Core Purpose: The central repository of integrated data. Components: Star schema: A common data warehouse design where a fact table is surrounded by dimension tables. Snowflake schema: A more complex design where dimension tables can have their own hierarchies. Cube structure: A multidimensional data structure used for OLAP (Online Analytical Processing) operations.



5. Metadata Layer Purpose: Stores information about data elements, their relationships, and data quality. Components: Data dictionary: Defines data elements, their attributes, and relationships. Data quality rules: Defines standards for data accuracy, completeness, and consistency. Business rules: Defines constraints and guidelines for data usage. Key considerations for data warehouse structure: . Denormalization: The process of adding redundancy to improve query performance. Data granularity: The level of detail in the data. Data consistency: Ensuring data accuracy and integrity across different sources. Data security: Protecting sensitive data from unauthorized access. By understanding these components and their roles, you can effectively design and implement a data warehouse that supports your organization's decision-making needs.