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. ⬅ Previous Page | ➡ Next Page