Enterprise Data Warehouse: An In-Depth Guide

Enterprise Data Warehouse is a central repository where an organization stores its Historical Business Data. The data is extracted from various data sources like Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) platforms, etc. An Enterprise Data Warehouse can have any one of the following architectures.

One-Tier Architecture

One-Tier Architecture
In One-Tier Architecture, the Reporting tools are directly connected to the Data Warehouse as shown in the above image. An Enterprise Data Warehouse (EDW) based on One-Tier Architecture is most suitable for businesses with small datasets as its performance decreases with an increase in the data volume.

Two-Tier Architecture

Two-Tier Architecture

Unlike the One-Tier Architecture, an Enterprise Data Warehouse with Two-Tier Architecture houses a Data Mart Layer between the Reporting and Data Warehouse Layer as shown in the above image. A Data Mart, in simple terms, is a smaller version of a Data Warehouse that focuses on a specific domain of your operational data.

A Data Mart draws data from a limited number of sources and is, therefore, faster than a Data Warehouse. In Two-Tier Architecture, the domain-specific queries are handled by Data Marts to enhance the overall performance of the Enterprise Data Warehouse.

Three-Tier Architecture

Three-Tier Architecture
The Three-Tier Enterprise Data Warehouse Architecture houses an Online Analytical Processing (OLAP) Layer between the Data Mart and Reporting Layer as shown in the image below.

The OLAP Layer stores data in multi-dimensional form using the OLAP Cubes. OLAP Cubes minimizes the amount of processing needed while you are navigating your data.

It is achieved by pre-processing and storing every possible combination of dimensions, measures, and hierarchies before performing the Data Analysis. This increases the performance of the Data Warehouse and results in effective Data Analysis.

Types of Enterprise Data Warehouse

There are majorly 2 types of Enterprise Data Warehouse:

  • On-Premise Data Warehouse
    On-premise Data Warehouse is the traditional method of Data Warehousing. Here, the organization is responsible for purchasing, setting up, and maintaining the complete Data Warehousing Solution which is based on the organization’s business model and requirements. Although it gives the overall Data Warehouse control to the organization, it is extremely heavy on the budget. It demands a high investment for the initial setup of necessary infrastructure (software and hardware). Furthermore, it is easy to scale up the resources of an On-Premise Data Warehouse as per the business requirements because of its limited hardware.
  • Cloud-based Data Warehouse
    It is the modern solution to Data Warehousing which uses Cloud technology to handle the ETL process. A Cloud-based Data Warehouse eradicates all the disadvantages of an On-Premise Data Warehouse such as limit in data storage capacity, the limit of scaling your resources according to your business requirements, a limit on how many users can work on the Data Warehouse before it gets overloaded, etc. With Cloud-based Data Warehouse, you can access your data from anywhere in the world, effectively store almost unbounded volumes of data, instantly scale up your resources as per the requirements, etc. Furthermore, it enhances the performance of Data Warehouse and results in more accurate insights.

Advantages of an Enterprise Data Warehouse

Here are some of the major advantages of an Enterprise Data Warehouse:

  • Data Standardization:Data extracted from multiple disparate sources need to be standardized (i.e. cleaned and transformed into a standard format) before being used for analytical purposes. An Enterprise Data Warehouse takes care of this.
  • Scalable:An Enterprise Data Warehouse is flexible to changes and easily scales your resources based on changes in your business requirements
  • Seamless Integration: You can seamlessly integrate your Enterprise Data Warehouse with Business Intelligence Tools and keep track of your Key Performance Indicators (KPIs).

Most Popular Enterprise Data Warehouses

Now, that you have a better understanding of an Enterprise Data Warehouse, let’s walk through some of the most popular Enterprise Data Warehouse:

  • Amazon Redshift

Amazon Redshift
Amazon Redshift is a Cloud-based Enterprise Data Warehousing Solution from Amazon Web Services (AWS). It provides you a Shared-Nothing Massively Parallel Processing (MPP) platform that makes it simple and cost-effective to analyze your data. It has a collection of computing resources known as Nodes which work independently and do not share the same memory for processing the queries.

  • Google BigQuery

Google BigQuery
Google BigQuery is a Cloud-based Enterprise Data Warehouse from Google Cloud Platform (GCP). Similar to Amazon Redshift, it provides Shared-Nothing Massively Parallel Processing which simplifies and automates data operations.

Its serverless architecture allows all the resources to work independently and helps in scaling your business on-demand. Google BigQuery also has the Time Travel feature that allows you to revert any changes made (in case of data loss) in a restricted period of 7 days.

  • Snowflake


Snowflake is a Cloud-based Enterprise Data Warehouse that works on Hybrid (Shared-Nothing and Shared-disk) elements simultaneously. It is one of the fastest Enterprise Data Warehouse and provides various options when it comes to accessing data.

Apart from JDBC and ODBC drivers which are used to run your queries, it has Spark Connectors which initiates an Apache Spark ecosystem and enables Spark to execute all your queries in Snowflake. This also enhances the performance of Snowflake.


This blog introduced you to Enterprise Data Warehouses and their significance. Furthermore, it also talked about some of the primary differences between On-Premise Data Warehouses and Cloud-based Data Warehouses.

In case you want to simplify Data Analysis by integrating your Operational Data Warehouses with any Data Warehouse of your choice (including Amazon Redshift, Google BigQuery, etc.) in real-time, you can explore Hevo Data.

Hevo Data supports 100+ data sources (including 30+ free data sources) and makes Data Migration fully automated and hassle-free.

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