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Classical Architecture of Data Warehouse

Last updated May 7, 2024

This is the classical data warehouse architecture I learned at the beginning of my career. And to this day, I like to model my data warehouse this way.


From Data Warehouse Blueprints: Business Intelligence in der Praxis : Schnider, Dani, Jordan, Claus, Welker, Peter, Wehner, Joachim: Amazon.de: BĂŒcher, September 2016, created Trivadis.

# Layers

In this overview, we’ll delve into each layer of a complete Data Warehouse (DWH) architecture and explore why this modeling approach is effective:

# Staging Area

Staging Area: This initial layer serves as the landing point for data from various source systems.

# Cleansing Area

Cleansing Area: Prior to integration into the Core, data undergoes cleaning in the Cleansing Area.

# Core

Core: Data from various sources converges in the Core, having passed through the Staging and Cleansing areas, and is stored long-term, often for years.

# Data Mart

Data Mart: Marts store subsets of Core data, optimized for user queries.

# Metatdata

Metadata: The foundation of the DWH system, metadata, is essential for its smooth operation.

While not every Data Warehouse adheres strictly to this structure, with some areas merged or renamed, the essential concept is to segment the system for task specialization. This segmentation facilitates data cleaning, integration, historization, and query handling, simplifying the transformation processes between layers.


Origin:
References: Medallion Architecture
Created 2023-04-27