Data Warehouse Architecture
A Detailed Explanation
Introduction to Data Warehouse
These are repositories of data compiled from various sources, including user, manufacturer, and third-party reports. Access to the data is made easier with tables and databases.
What is Data Warehouse Architecture?
A collection of interconnected databases that allows data to be accessed, analyzed, and stored. It includes 3 main components; data warehouse, analytical framework, and integration layer.
Data Warehouse Architecture Properties
1. Analytical and transactional processing should be kept separate.
2. Maintain data security.
3. Architecture should be able to be extended.
4. Simple and straightforward should be the goal.
Types of Data Warehouse Architectures
Single-layer structures are designed to keep data space to a minimum. They can be used for batch and real-time processing but are not implemented in real-life systems.
In this, analytical process is separated from business process, allowing for increased control and efficiency. The data can also be better understood, enabling more informed decisions.
It is employed in source layer, reconciled layer, and data warehouse layer. Reconciler sits between source data and data warehouse, and focus primarily on data integrity, and accuracy.
1. Speeds up Data Retrieving.
2. Helps in error identification and correction.
3. Support ETL processes.
4. Enables real-time analytics.
Advantages of Data Warehouse Architecture
1. Require a lot of maintenance.
2.Hight maintenance costs.
3. Automated extraction cannot guarantee data cleaning and validation.
4. Hidden problems of data sources
Disadvantages of Data Warehouse Architecture
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