Explaining Data Warehouse Architecture in Detail – IQCode

Data Warehouse Architecture: Types, Advantages, and Disadvantages

A data warehouse serves as a repository of both live and archived data, making it easily accessible as well as improving effectiveness and efficiency. It contains organized data collected from various sources such as third-party vendors and manufacturers. In this article, we will explore the different types of data warehouse architectures, including the single-tier, two-tier, and three-tier architectures. We will also discuss the advantages and disadvantages of implementing a data warehouse architecture and provide additional resources for further reading.

Data Warehouse Architecture

Data Warehouse architecture can be categorized into three approaches – single tier, two-tier, and three-tier. Among these, the Single-tier architecture is rarely used, while the Two-tier and Three-tier architecture are popular.

The Three-tier Data Warehouse Architecture is composed of Top, Middle, and Bottom Tiers, with each tier having its unique function.

– The Bottom Tier is where the raw data is stored in a relational database system and cleaned, transformed and loaded by back-end tools.
– The Middle Tier is responsible for abstracting OLAP from the end-user through an OLAP server that could be either ROLAP or MOLAP-based.
– The Top Tier is the front-end client layer where the data is presented to the end-user. It must process data quickly and is structured and validated in a way that allows for easier data profiling and analytics.

When selecting an architecture for Data Warehouse, scalability must be the top priority, as it should be able to store large amounts of data in a small space.

Data Warehouse Architecture Properties

A data warehouse system must possess the following architecture features:

  • Separate analytical and transactional processing as much as possible
  • Demonstrate scalability by processing huge volumes of data quickly in various formats and streaming it to different destinations while protecting confidentiality and integrity
  • Extend the architecture to include new functionality with existing APIs
  • Implement data security controls for source and perimeter access
  • Ensure user-friendliness and easy management for efficient data use


//Sample code for demonstration
class DataWarehouse {
constructor(data) {
this.data = data;
}

separateProcessing() {
//Separate analytical and transactional processing
}

processAndStream() {
//Demonstrate scalability to handle huge quantities of data in various formats and stream it to different destinations
}

extendArch() {
//Extend the architecture to include new functionality with existing APIs
}

applySecurity() {
//Implement data security controls for source and perimeter access
}

userFriendly() {
//Ensure user-friendliness and easy management for efficient data use
}
}

let myDW = new DataWarehouse(myData);
myDW.separateProcessing();
myDW.processAndStream();
myDW.extendArch();
myDW.applySecurity();
myDW.userFriendly();

Data Warehouse Architecture Types

In the realm of data warehousing, there exist three fundamental architecture types.

Code:

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Single-Tier Architecture

Single-tier architecture is not used in real-time systems but in batch and real-time processing. Data is first converted to a suitable format in this architecture, which is single-threaded, and then transferred to the actual real-time system. It is currently the most preferred way to process operational data. However, note that single-tier architectures are not implemented in real-time systems.

For reliable processing and to avoid security breaches, the data storage and processing middleware must assess the data quality before handing it to the analytical engine for relevant information transformation. Failure to perform these steps increases the risk of a security breach where a hacker can alter data and extract valuable information, such as in the case of credit score calculation.

Two-Tier Architecture

Two-Tier architecture separates analytical and business processes, offering better control and efficiency. It provides a clear understanding of data and supports informed decision-making.

Data Flow
This architecture involves a four-stage data flow where physical sources are separated from data warehouses by the two-layered architecture.

Data Integrity
The data source is crucial for ensuring data integrity in a warehouse. A data warehouse stores information that can be searched and analyzed.

ETL Process
Data staging is a crucial process that significantly reduces the time it takes to conduct ETL operations on a large data set. Data can be extracted from various storage systems, transformed, and loaded into a data warehouse through ETL tools. Monitoring systems, provisioning data, and making decisions based on data are all data warehouse functions that are performed through ETL.

Data Warehouse Metadata
Metadata provides valuable information for a data warehouse administrator to decide what data to keep and what to delete. Consistency must be maintained in a data warehouse so that application developers and users can create tables and reports once

Data Profiling
Data profiling assists in validating data integrity and presentation standards. It offers advanced analytics such as real-time, batch reporting, visualizations, and rating functions. This is not just a data warehouse, but rather a live data platform that handles massive amounts of data; therefore, tracking data changes, scalability, and performance is crucial.Three-Tier Architecture for Data Warehousing

This architecture employs a three-tier structure for the source, reconciled, and data warehouse layers. The reconciled layer ensures data integrity, accuracy, and consistency before moving to the data warehouse. A web-based data warehouse refresh tool is best for frequently updated data. This structure is suited for long-life cycle systems and is data-driven. The extra data review and analysis layers don’t require extra storage space.

Code:

Three-Tier Architecture for Data Warehousing

This architecture employs a three-tier structure for the source, reconciled, and data warehouse layers. The reconciled layer ensures data integrity, accuracy, and consistency before moving to the data warehouse. A web-based data warehouse refresh tool is best for frequently updated data. This structure is suited for long-life cycle systems and is data-driven. The extra data review and analysis layers don’t require extra storage space.

Advantages of Data Warehouse Architecture

A data warehouse is a crucial tool for businesses to collect and analyze huge amounts of data. Here are some benefits of using a data warehouse:

– Data mart provides consistency, governance, and a common data access strategy for the warehouse.
– The process of change in implementing a new system requires identifying problems and mapping a plan to solve them, followed by testing and stakeholder validation.
– Data warehouses support ETL processes, delivery of data to CRM systems, and streamline decision-making.
– Using NoSQL databases like MongoDB or GARIA can increase the speed and scale of data warehouses, enabling real-time analytics and increased profitability.Disadvantages of Data Warehouse Architecture

A data warehouse requires significant effort to maintain, collect, process and analyze data which may not be justified by the ROI. While ETL tools can automate the extraction process, they do not guarantee clean and validated data. It is best to do both manual and automated tasks in sequence. Proper data integration is essential to ensure the accuracy of data within the warehouse. The warehouse infrastructure must support the analysis of massive amounts of data and the storage of data in a cost-effective manner. Carefully considering the data source is important and an organization must work to integrate multiple sources of data.

Data Warehouse Architecture

A data warehouse architecture stores, organizes, and analyzes data through interconnected databases. It consists of three main components: a data warehouse, an analytical framework, and an integration layer. The data warehouse is the central repository, while the analytical framework processes data into tables. An integration layer connects databases and makes them accessible to other applications. A data warehouse architecture optimizes the IT infrastructure by organizing, storing, and reducing redundant storage spaces, making it easier to find, access, and analyze data.

Additional Resources

Here are some helpful resources related to data warehousing:









These resources can aid in preparing for data warehouse interviews and learning about data warehousing tools, characteristics, and components.

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