Enterprises tend to make operational decisions based on their previous experiences. As the human brain stores millions of pieces of information regarding past experiences to compile and make future decisions, enterprise databases also need to store trillions of bits of data for the applications to use and make strategic decisions.

Considering this requirement, an enterprise data warehouse or EDW is a collection of databases that helps to centralize the business info from various sources and applications to make it available for analytics across the enterprise. Nowadays, it is possible to house such gigantic EDWs on the cloud also along with on-premise.

In this article, we discuss the enterprise data warehouses, their types, and how they function to process data at best. We may see how these enterprise warehouses are different from the basic databases and the kinds of enterprise data warehouses and their function. This attempt aims to provide you with enough information about the business value of these database architectures and to explain the conceptual approach to building data warehouses.

Enterprise Data Warehouse (EDW)

An EDW functions like a corporate repository that stores and manipulates the historical business data for functional and analytical purposes. These data are usually stored from different systems as the enterprise resource planning systems, customer relationship management systems, physical records, and other files. To prepare data for future analysis, these needed to be placed in an integrated storage facility, which is easy to query and manipulate. This way, any given business units can easily ask and analyze information using EDW from multiple angles.

Using such data warehouses, enterprises can now manage a vast store of data sets without the need to administer multiple databases for storage. This practice of EDW is also future proof in the arena of data storage for business intelligence. BI or business intelligence is a unique set of technologies/methodologies, which can help transform raw data into actional decision making insights. With EDW being an essential part of it, this system can function as a healthy human brain in storing information and processing it wisely when needed.

Differences between usual data warehousing an EDW

Primary data warehouses are databases that are directly connected to the raw-data sources through integration tools at one end of it, with many analytical interfaces integrated on the other end. Any given data warehouses offer storage facilities with mechanisms for data transformation, movement, and presenting it to the end-users. The significant difference between the general data warehousing and EDW is on the functionality and structural diversity parts. Due to the compact size and structure of these large data stores, EDWs have usually decomposed too much smaller databases with which the end-users may be very comfortable in terms of querying these smaller databases than approaching a giant store. Considering this fragmentation, we focus on the enterprise warehouses to better cover a broader spectrum of functionality.

However, the size of a warehouse does not define its technical complexity, the requirements for analytical and reporting capabilities, the number of data models, and the data itself. So, to understand what makes a warehouse a warehouse, let us dive into its core concepts and functionality.

Concept and function of Enterprise Data Warehouses

Irrespective of the size and features, at the heart of each data warehouses, there are some essential standard functions and concepts. These act as the supporting pillars of the warehouse technology. As we had seen above, being an ultimate storage space, enterprise data warehouses are a unified repository of all types of business data, whichever occurs in an enterprise. For those who find it difficult to understand the structure and concept of database admiration in-house, consulting services offered by providers like comes as a boon.

EDW can also source data from the original storage spaces as CRMs, Google Analytics, IoT devices, Big Data stores, etc. If the data is scattered across various systems, then usually, it may be unmanageable. The purpose of EDW here is to provide a likeness of the actual data source in a unified repository. As always new, the relevant data generated both outsides and inside the company. The flow of this data requires an infrastructure dedicated to it before it enters into a warehouse.

EDW can also store structured data. Data stored in the data warehouse is in a standardized format. So, it is possible for the users to easily query it through various BI interfaces and the form reports. This makes the data warehouses different from the data lakes. In data lakes, data is stored in the unstructured form, which can be used for analytical data purposes. However, unlike the warehouses, Data Lake may use data engineers and scientists more to work with the raw data in huge volumes.

Enterprise data warehouses also contain subject-oriented data. The primary focus of an enterprise warehouse is to deal with business data that can relate to various domains. To understand what precisely the given data relates to, it should be structured around a particular subject, as a standard data model. The examples of the item can be the sales region or the total number of sales of the given product. Adding to it, metadata also gets added to explain it in detail as to where each piece of info comes from and gets stored.

How does the data warehouse function?

Experts from the USA’s esteemed company, state that a data warehouse functions like a core repository where the data comes from more than one source. This data goes into the data warehouse from relational databases and other transactional systems. This data can be-

  • Unstructured
  • Semi-structured or
  • Structured

The above data is later processed, changed, and stored in such a manner that users can easily access this processed information in the data warehouse via BI tools, spreadsheets, and SQL clients. The data warehouse can combine all the information arriving from different data sources to place them in a single extensive system.

When this information is merged in a single platform, it can quickly analyze all its customers holistically and straightforwardly. This allows the business to get all the information it needs. It makes the process of data mining possible. This means hidden patterns in the obtained data can easily be obtained, and this leads to better sales and profits with success for the business.

What are the 3 types of data warehouses?

The following are the 3 types of data warehouses-

  • Enterprise Data Warehousing – This is a centralized repository of information. It offers support services for decision making across the total enterprise. It gives the unit a single unified approach for the organization and the representation of data. It provides the enterprise with the ability to divide and classify the data as per the subject. Based on this classification, it allows access to these divisions.
  • Operational data stores – This is often referred to as ODS and is excellent for businesses where OLTP systems and data warehousing are not supported for their reporting needs. Real-time refreshments of data can be obtained in this system, and it is ideal for any routine task like storing Employee records of an organization.
  • Data Mart – This refers to any subset of the data warehouse. It is created and made for a specific line of businesses like finance, sales, and the like. In this type of data warehouse, the information or the data can easily be collected from the source.

Time dependency and nonvolatility

Usually, data collected is historical, which denotes the past events. To know when and for what duration the particular data-related tendency occurred, most of the stored data is usually divided into various periods. Once the information is put into the warehouse, it never gets deleted. The data can be modified, manipulated, and updated due to the change of source, but it is not meant to be erased. As we deal with historical data, deletions can be counterproductive because of analytics. However, general data revision can still be occurring once in a few years to get rid of the most irrelevant data.

The business owners may be confused about thinking about enterprise data warehousing with the number of options available. It is essential to consult with the experts in data warehousing and Business Intelligence to help you understand the technical aspects of it and choose the best available options.

Author bio –

Ariya Stark is a web developer and experienced professional in database management and administration. She says you must deploy credible companies like to help you maintain and secure any database system with success!


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