Data warehouse structure

Data Warehouse Structure is a centralized repository of data collected from various sources, designed to support business analysis activities. It’s structured to provide a unified view of an organization’s data, making it easier to analyze trends, patterns, and relationships.

Key Components of a Data Warehouse

1. Data Source: The origin of the data such as transactional systems customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and external sources.
2. Extraction, Transformation, and Load (ETL): The process of Phone Number extracting data from source systems, transforming it into a consistent format, and loading it into the data warehouse.  
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3. Data Mart: A subset of a data warehouse, focused on a specific business area or subject matter.
4. Metadata: Information about the data, including its source, meaning, and quality.

Data Warehouse Architecture

There are two primary architectural styles:

1. Star Schema:

   • Fact Table: Contains measurements (metrics) related to a specific business event or process.
   • Dimension Tables: Contain descriptive 2024 Iran Telegram Users Library Powder attributes about. the fact table, such as time, product customer and location.

2. Snowflake Schema:

   • Fact Table: Same as in star schema.
   • Dimension Tables: Can have hierarchical relationships, allowing for more granular analysis.
   • More complex than star schema but offers greater flexibility.

Dimensional Modeling

Dimensional modeling is a technique used to AFB Directory design data warehouses. It focuses on creating fact tables and dimension tables that are optimizibg for analytical queries.

Key concepts in dimensional modeling:

• Granularity: The level of detail in the data.
• Conformance: Ensuring data consistency across different sources.
• Slowly Changing Dimensions (SCDs): Handling changes in dimension attributes over time.

Benefits of Data Warehouses

• Improved Decision Making: Provides a comprehensive view of data for better insights.
• Enhanced Business Analysis: Supports various analytical techniques.
• Operational Efficiency: Streamlines reporting and analysis processes.
• Data Governance: Ensures data quality and consistency.

• Example of a Data Warehouse

  A retail company might have a data warehouse with fact tables for sales transactions, customer information,