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6. Data Mart Layer (DM)
In a data warehouse, layers are a crucial aspect of its logical structure. Users have the ability to define a variety of layers, each serving a specific purpose. Below are six primary types of layers commonly used in a data warehouse architecture, along with their functions and interconnections.
Visual Guide – Data Warehouse Layers (Canva)
This configuration facilitates an efficient workflow, transforming raw data sources into insightful, user-accessible information.
Each layer plays a distinct role in the data journey—from acquisition to end-user presentation—supporting governance, transformation, historization, and analytics.
- Purpose: Acts as the foundational logical data layer containing external data sources.
- Characteristics:
- Not part of the actual data warehouse storage.
- Serves as the entry point for incoming external data.
- Tables and transformations cannot be created in this layer.
- Also Known As: Import Layer
- Purpose: Loads and structures raw data from the Source Layer into tables for further processing.
- Characteristics:
- Temporarily stores incoming data.
- Frequently refreshed with the latest imports.
- Prepares data for historization and persistence.
- Purpose: Begins the commitment to data historization and traceability.
- Characteristics:
- Stores data from the Staging Layer persistently.
- Maintains historical records of changes.
- Considered the first "true" layer of the data warehouse.
- Purpose: Applies additional logic and refinements to the data.
- Characteristics:
- Optional, but useful for cleansing, deduplication, or complex business logic.
- Ensures high data quality and consistency.
- Acts as a bridge between raw and modeled data.
- Purpose: Converts structured data into analytical models (e.g., facts and dimensions).
- Characteristics:
- Core repository of business-ready data.
- Supports advanced querying, reporting, and data analysis.
- Purpose: Provides business users with access to relevant datasets in a user-friendly structure.
- Characteristics:
- Often adopts star schema or other analytical models.
- Optimized for reporting tools and dashboards.
- Represents the interface between the data warehouse and the end-user.
Together, these layers enable a modular and governed approach to building scalable and maintainable data warehouse solutions in AnalyticsCreator.
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