Introduction to Types of Data Warehouse

Types of Data Warehouse


A data warehouse consists of highly structured data that is collected from various sources and combined using different methods. Types of Data warehouse are the ways to implement data in a structured manner. You can also learn about what is data science, data engineering, scope and future of data engineering, data analytics, and much more.

What is Data Warehousing?

A data warehouse is a centralised archive of old and new data, which is being collected and stored with a purpose to be used in the future.

Whereas Data Warehousing is the process of compiling and administering the collected data to help your organisation answer important business questions and gain key insights.

It is a combination of various technologies that will help you achieve your data’s efficient and strategic use.

Types of Data Warehouse Architecture

A data warehouse architecture helps in establishing a complete data communications system. There are three important types of data warehouse architecture:

  • Single tier Architecture: This is focused on generating a dense data set. This type is not suitable for an organisation with complex data set requirements.
  • Two-tier Architecture: This type is more efficient at handling data compared to single-tier architecture. It uses both a system and a database server. It is best suited for small organisations.
  • Three tier Architecture: This is the most commonly used architecture and is also known as (OLAP) Online Analytical Processing. It includes an OLAP server that converts data sets into arrangements to be analysed and give key insights easily.

Once you know which data warehouse architecture is best suited for your organisation you can move onto building your data warehouse.

Basic Concepts of a Data Warehouse

Before we discuss the different types of data warehouse understanding the different data warehouse basic concepts is an important step towards building and efficient data warehouse. There are four main basic concepts of a data warehouse that your organisation should be familiar with:

  • Load Manager: This is the front component and is responsible for preparing the data before it enters the various warehouse types available.
  • Warehouse Manager: It is responsible for managing the data once it enters the data warehouse. It then analyses, transforms, and aggregates the data present in the warehouse.
  • Query Manager: This is the backend component and is responsible for all the management related user queries.
  • End User Access tools: This is further sub-categorized into 5 groups mainly:
  1. Data Reporting
  2. Query Tools
  3. Application Development
  4. EIS Tools
  5. OLAP tools and Data Mining tools

Along with the knowledge of the basic concepts of data warehouse your organisation should also have a basic understanding of how a data warehouse functions. This is where data warehouse schemas come into play.

Similar to how a database uses relational models, a data warehouse uses schemas. A schema in simple words is the logical description of the all the data sets available, such as the name and description of all data records.

There are three types of schemas in data warehouse.

  • Star Schema
  • Snowflake Schema
  • Fact constellation Schema

Types of Data Warehouse

There are 3 types of Data Warehousing:

  • Enterprise Data Warehouse
  • Operational Data Store
  • Data Mart

1. Enterprise Data Warehouse

An Enterprise data warehouse is a database that combines several functional areas of an organization in a unified way. It helps in storing data from various sources and categorizes them accordingly, so it is easily accessible across your organization. An enterprise data warehouse will have inbuilt procedures for extracting, converting and analyzing data.

2. Operational Data Store

An operational data store, also known as an Operational Decision Support system (ODS), is a database used when neither Online Transactional Processing (OLTP) nor data warehouse can satisfy your organization’s requirements. The data warehouse is refreshed in real-time, and the redundancies present are accounted for and resolved. For example, ODS is used for employee information database.

3. Data Mart

An independent data mart can gather data directly from the source. It is built for a specific field of business, such as finance, marketing, or sales.

Data marts are further classified into three categories:

  • Dependent Data Mart
  • Independent Data Mart
  • Hybrid Data Mart

A data mart is also much more cost effective compared to a full data warehouse.


Himanshu Mishra

Technology Head
Himanshu is an entrepreneur with 17+ years of overall experience in strategic and advisory roles in Senior Management, IT program management, Quality Assurance, and ERP implementations. He has invested in companies focused on Digital Technologies and Healthcare industries. He has previously worked in domains like: Technology, Finance, Marketing, Media & Entertainment and Quality Assurance.

Passionate about technology, innovation, and music, he always keeps up with market trend…

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