Data Warehouse Architecture

Basic Data Warehouse Architecture

Operational System

In data warehousing, an operational system is a system that processes an organization's day-to-day transactions.

Flat File System

A flat file system is a collection of files in which each file must have a unique name.

Metadata

The metadata contains information about other data but not the data itself, such as a message's text or an image's content. We use metadata to direct a query to the most relevant data source.

Raw data 

Raw data is a set of data that has not yet been processed and delivered from a specific data entity to the data supplier and has not been processed by machine or human. This information is gathered from various online sources to provide detailed insight into users' online behavior.

Summary Data

Data summary is a simple term for a brief conclusion to a large theory or paragraph. This is frequently the case in which analysts write the code and then declare the ultimate end by summarizing data. In data mining and processing, a data summary is critical.

Staging Area Data Warehouse

Before putting our operational data into the warehouse, we need to clean and process it. Although data warehouses use a staging area, we can do this programmatically.

A staging area is a location where data is processed before entering the warehouse. It makes data cleansing and consolidation easier for operational methods that use data from multiple sources, especially for enterprise data warehouses where all relevant data is consolidated.

Data Warehouse Architecture with Staging Area and Data Marts

We can customize the architecture of our warehouse for multiple groups within our organization by adding data marts.

The data mart is a part of the storage component. It stores the information of an organization's particular function, which is handled by a single authority. Depending on the functions, there can be as many data marts in an organization. We can also say that a data mart is a subset of the data in a data warehouse. 

The above diagram illustrates an example in which purchasing, sales, and stocks are all separated. In this case, a financial analyst wants to mine historical data to predict customer behavior or analyze historical data for purchases and sales.

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Data Warehouse Architecture Properties

Subject-Oriented:

Data is organized around subjects (customers, products) rather than applications or transactions.
Facilitates answering business-oriented queries.

Integrated:

Integrates data from various sources into a unified, consistent format.
Resolves inconsistencies in data naming, encoding, and other attributes.

Non-Volatile:

Once loaded into the Data Warehouse, data isn't expected to change.
Ensures historical data consistency for accurate analysis over time.

Time-Variant:

Stores historical data, allowing trend analysis.
Time-related data changes are tracked to provide temporal insights.

Three-Tier Architecture:

Typically consists of three tiers
Data Source Layer: Where data is collected from various external and internal sources.
Data Storage Layer: Incorporates a Data Warehouse database where data is cleansed, transformed, and loaded (ETL process).
Presentation Layer: Where data is presented to end-users in a useful format, often through tools for querying and reporting.

Types of Data Warehouse Architectures

Data Warehouse architectures are mainly of three types: 

Single-Tier Architecture

The data storage and data access components are stored on the same server in this architecture, which has a single tier defined for the data warehouse server. 
It works well for standalone or small-scale data warehouse deployments with limited user access requirements and data volumes.

Two-Tier Architecture

The client tier and the server tier are the two layers that make up this design. 
While the server tier contains the data warehouse and controls data processing and storage, the client tier offers user interfaces and tools for data querying and analysis. 
It offers superior performance, scalability, and concern separation over single-tier architecture.

Three-Tier Architecture

The client tier, middle tier, and server tier are the three tiers into which the three-tier architecture splits the data warehouse environment.
Applications and user interfaces are part of the client tier; middleware, or application servers, handle business logic and data processing; and the data warehouse is housed on the server tier.
Because the appearance, application, and data layers are kept apart, it provides better scalability, flexibility, and maintainability.

Advantages of Data Warehouse Architecture

1. Improved Decision-Making:
   Centralizes and harmonizes data from various sources.
   Provides a solid foundation for analytics and reporting.
   Enables historical analysis and trend forecasting.
    
2. Data Quality and Consistency:
   Ensures data consistency across the organization.
   Implements data cleansing and quality checks.
   Provides a single version of the truth.
    
3. Enhanced Business Intelligence:
   Supports a wide range of BI activities.
   Facilitates advanced analytics, data mining, and machine learning.
   Empowers users with self-service analytics and reporting capabilities.
    
4. Performance and Scalability:
   Optimizes data retrieval and analysis performance.
   Scalable architecture grows with the evolving data and analytics needs.
   Supports ad-hoc queries and scheduled reporting efficiently.
    
5. Time and Cost Efficiency:
   Reduces the time and effort required for data preparation and analysis.
   Streamlines the ETL (Extract, Transform, Load) processes.
   Lowers the total cost of ownership in the long run by maintaining a centralized data repository.

Disadvantages of Data Warehouse Architecture

1. High Initial Costs:
   Significant investment is required for hardware, software, and expertise.
   Costly to set up, configure, and maintain.
    
2. Complex Implementation:
   Requires thorough planning, design, and expertise.
   Integration with existing systems can be challenging.
    
3. Data Latency:
   Data may not be available in real time due to batch processing.
   ETL processes can introduce delays in data availability.
    
4. Potential Overhead:
   Requires additional systems and processes.
   It can add to the operational overhead and require more management.
    
5. Limited Historical Data:
   Depending on storage capacity, there might be limits on how much historical data can be stored.
   Pruning of older data may limit historical analysis.

Frequently Asked Questions

What is data warehouse architecture?

Data Warehouse Architecture organizes data storage, retrieval, and management for analytics. It typically has a three-tier structure: data source layer, data storage layer, and presentation layer, ensuring data integration, consistency, and accessibility for insightful decision-making.

What are the 3 data warehouse architectures?

Data Warehouse architectures are mainly of three types. These are Single-Tier Architecture, Two-Tier Architecture, Three-Tier Architecture. 

What are the 3 models of data warehouse?

The three data warehouse models are as follows: 
Enterprise Data Warehouse (EDW): All organizational data is centrally stored. 
Data Mart: An EDW subset specialized to specific divisions or operational tasks. 
Operational Data Store (ODS): An operational data integration and reporting database that is updated in real-time or almost real-time.

What is process architecture in data warehouse?

The design and organization of data processing processes and activities inside the data warehouse environment is known as process architecture in the context of data warehousing. To ensure effective data integration and delivery, it includes data extraction, transformation, loading (ETL), scheduling, monitoring, and management.

What is data warehouse architecture in data mining?

In data mining, data warehouse architecture refers to how data warehouses are set up and arranged to make data mining procedures more efficient. To extract useful insights from massive datasets, it consists of parts including data sources, ETL procedures, data storage, data mining algorithms, and display layers.

Conclusion 

In this article, we have extensively discussed the architecture of the data warehouse. The article explains the details of the architecture of the data warehouse with the help of diagrams.

We hope that this blog has helped you enhance your knowledge regarding the architecture of the Data warehouse and if you would like to learn more, check out our article on Data Mining Vs. Data Analytics. You can read our Java language articles by clicking Jave Archives. 

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