Understanding the Basics of Data Warehouse and its Structure

Harini C 21 Feb, 2023
5 min read


Nowadays, the corporate environment changes according to technology. Organizations are converting them to cloud-based technologies for the convenience of data collecting, reporting, and analysis. This is where data warehousing is a critical component of any business, allowing companies to store and manage vast amounts of data. It provides the necessary foundation for businesses to make informed decisions and gain insights from their data. Data warehousing has become even more important with the increasing demand for more comprehensive data analysis.

Learning Objectives

  1. Understanding the Basics
  2. What are the various types of data warehouses and their characteristics?
  3. Understanding the three-tier architecture of data warehouse.
  4. What is the need for a data warehouse?
  5. Advantages and Disadvantages

This article was published as a part of the Data Science Blogathon.

Table of Contents

What is a Data Warehouse?

A data warehouse is a database used for reporting and data analysis. It is a centralized repository for storing, integrating, and analyzing large amounts of data from various sources.  A data warehouse typically stores data from multiple sources in a format that can be easily analyzed. Subjects, such as customers, products, or sales, typically organize the data in a data warehouse.

A data warehouse can be used to support a variety of reporting and analysis needs, such as financial reporting, sales analysis, and marketing analysis. It can also support operational decision-making, such as inventory management and capacity planning. This is a valuable asset for any organization that needs to make data-driven decisions. It can help an organization make better decisions by providing a centralized data repository that can be easily accessed and analyzed.

Data Warehouse

Source: bi4dynamics.com

Various Types of Data Warehouses

There are several types of data warehouses, each with its own unique characteristics and use cases:

  1. Enterprise Data Warehouse (EDW):A centralized repository that collects data from various sources within an organization to support decision-making across the enterprise. EDWs are typically large and complex and are used by multiple departments and business units.
  2. Operational Data Store (ODS):An intermediate store for real-time data that provides a consolidated view of data from various operational systems for reporting and analysis. Unlike EDWs, ODSs are optimized for real-time performance and are typically used for near-real-time reporting.
  3. Data Mart:A subset of an EDW optimized for a specific department, business unit, or line of business. Data marts are smaller in size and less complex than EDWs and are used to meet individual business units’ specific reporting and analysis needs.
  4. Real-time Data Warehouse: A data warehouse optimized for real-time data processing and analysis. Real-time data warehouses are typically used in time-sensitive industries such as financial services and telecommunications.
  5. Cloud Data Warehouse: A data warehouse hosted on a cloud-based infrastructure, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud. Cloud data warehouses provide scalability, flexibility, and cost-effectiveness compared to traditional on-premises data warehouses.
  6. Hybrid Data Warehouse:A data warehouse that combines elements of both traditional on-premises data warehouses and cloud-based data warehouses. Hybrid data warehouses can take advantage of the benefits of both approaches, such as improved performance, scalability, and cost-effectiveness.

Source: Guru99

Data Warehouse Architecture

The three-tier architecture of a data warehouse is a common design pattern that separates the system into three distinct layers:

  1. Bottom-Tier:The bottom layer, or the data storage layer, stores large amounts of raw data and is optimized for efficient data retrieval. This layer typically consists of relational databases or specialized data storage systems.
  2. Middle-Tier: The middle layer, or the data integration layer, integrates and transforms the raw data from the bottom layer into a format that the top layer can use. This layer includes Extract, Transform, Load (ETL) processes, data cleansing, and data quality checks.
  3. Top-Tier: The top layer, or the data presentation layer, presents the integrated and transformed data to users through reporting, analysis, and data visualization tools. This layer includes OLAP (Online Analytical Processing) cubes, data dashboards, and business intelligence applications.

By separating the data warehouse into these three layers, organizations can optimize each layer for specific tasks and improve the performance and scalability of the system.

Data Warehouse

Source : educba.com

By separating the data warehouse into these three layers, organizations can optimize each layer for specific tasks and improve the performance and scalability of the system.

Why Do We Need a Data Warehouse?

Data warehouses are used to support business decision-making by providing a centralized repository for storing, integrating, and analyzing large amounts of data from various sources. Here are some of the key advantages of using a data warehouse:


  1. Improved Data Quality: Data warehouses help improve data quality by standardizing and transforming data from various sources into a consistent format. This can help to reduce errors and improve the accuracy of business decisions.
  2. Centralized Repository: Data warehouses provide a centralized repository for storing and managing data, which makes it easier to access, analyze, and share data across an organization.
  3. Improved Business Intelligence: Data warehouses provide a foundation for advanced business intelligence and analytics, allowing organizations to gain deeper insights into their data and make informed business decisions.
  4. Scalability: Data warehouses can be designed to scale as the amount of data grows, making it possible to accommodate increasing amounts of data over time.
  5. Performance: Data warehouses are optimized for fast data retrieval and analysis, allowing organizations to quickly access and analyze large amounts of data to support business decision-making.


  1. Complexity: Data warehouses can be complex to set up and maintain, requiring specialized knowledge and expertise.
  2. Cost: It can be expensive to implement and maintain, particularly for large enterprises with complex data requirements.
  3. Maintenance: It requires ongoing maintenance and management, including regular updates to data, ETL processes, and hardware.
  4. Data Latency: It can introduce latency in the data integration and analysis process, particularly for real-time data needs.
  5. Limited Flexibility: Data warehouses can be inflexible, as they are designed to support specific business requirements and may not be easily adapted to changing needs or requirements.


In conclusion, data warehouses play a critical role in supporting business decision-making by providing a centralized repository for storing, integrating, and analyzing large amounts of data from various sources. The advantages include improved data quality, centralized repository, business intelligence, scalability, and performance. However, data warehouses also have limitations, including complexity, cost, maintenance, data latency, and limited flexibility.

Organizations must carefully consider their data needs, requirements, and budget when implementing a data warehouse. Sometimes, a data warehouse may not be necessary or cost-effective, and alternative solutions such as data lakes or cloud-based data storage and analysis services may be more appropriate.

Regardless of the specific solution, it is important for organizations to have a clear understanding of their data needs and requirements to make informed decisions about how to manage, store, and analyze their data effectively.

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Harini C 21 Feb, 2023

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