What is Data Warehousing? Concepts, Features, and Examples 2024

Data Warehousing

The public is constantly bombarded with ever-changing buzzwords and phrase terms, specific industry terminology, and technical terms that can take time to stay up-to-date. Of all the phrases, have you asked yourself what exactly you mean by Data Warehouse? If so, you will discover the answer in this article.

In the current business climate, organizations must provide solid reporting and analysis of massive amounts of data. The data businesses collect must be analyzed and integrated to offer different aggregates, from the customer experience to integration with partners and top-level business decision-making. Data warehousing can help simplify the reporting and analysis processes—data growth, which, in turn, boosts the demand for data warehouses for managing corporate data.

We’ll examine the crucial notions of a data warehouse to understand the importance of data storage better.

What Is Data Warehousing?

Data warehouses are information management software designed to assist and simplify market intelligence (BI) and data analysis. They are built to monitor and search their data, often storing massive amounts. Data warehouses’ data are usually gathered through various channels, such as program log files and transactional software. Data warehouses are devices that gather and store vast amounts of information from various sources. The system’s analytical capabilities allow businesses to obtain significant business information to make better decisions. The importance of warehouses for organizations is evident.

In contrast to other programs commonly utilized in Data Lake and data science, which allow users to carry out analytical tasks and analysis, data warehouses can’t examine data by themselves. Instead, they depend upon data analysis and querying tools such as SQL. It is important to remember that a data warehouse stores unique data from competitors, including lakes, due to an established structure and organizational system. However, various methods and techniques are used to create the data warehouse.

Understand The Workings Of Data Warehousing

The data warehouse (DWH) collects information from various sources, typically the relational type, and converts it into a multidimensional structure for analysis by business intelligence software. Data may come in semi-structured or structured and unstructured formats, and an appropriate processing process is required to enable proper analysis. This makes the DWH a dynamic environment where many operations are performed to facilitate efficient analysis.

Data is gathered first from various sources before being incorporated into a single database during data warehousing. This consolidates customer information through point-of-sale systems, websites, and other sources to better understand the client base. After collection, data is processed, sorted, and then stored. The processed data is arranged into tables according to their type and format. Particular attention is paid to data security, such as sensitive information about employees.

When data is stored, it provides insight into customer behavior patterns and market trends. Data warehouses’ primary function is to manage data effectively and efficiently. They also assist in making informed business decisions. As we said, data warehouses function broadly regardless of the method you use to create them.

Characteristics Of Data Warehouse

The most important features of a warehouse for data are:


Data warehouses are subject-specific because they offer information based on a topic instead of the entire procedures of a company. The subjects include sales, promotion, or inventory. For example, creating an information warehouse that focuses on sales is necessary if you wish to study your business’s sales figures. A warehouse like this would give helpful information, such as “Who was your most valuable customer in the last year?’ and ‘Who could be your top customer over the next year?


A data warehouse is created by integrating data from various sources into a unified format. The data must be arranged inside the warehouse in a standardized and universally accepted way regarding naming, format, and even coding. This will allow for practical data analysis.


Once data is entered into the data warehouse, it must remain the same. The data warehouse can be read-only. Data from the past is not deleted when new data is added, making it easier to determine circumstances and events.


The data stored in a data warehouse are recorded with the help of the element of time, either implicitly or explicitly. The time variance within the Data Warehouse is exhibited in the Primary Key, which must include a date similar to the week, day, or month.

Database Vs. Data Warehouse

Though a data warehouse and a traditional database have similarities, they do not have to have the same concept. One of the main differences is that the data is gathered for different reasons. In a data warehouse, the data is collected at a large scale to enable analysis. Data warehouses offer real-time information, as they hold information that can be used to perform large-scale analysis queries. Data warehouse illustrates an OLAP or an online database query answer system. 

Types Of Data Warehouse

The most important types of data warehouses include Enterprise Data Warehouses (EDW), Operational Data Stores (ODS), and Data Marts.

Let’s look at each of them.

Enterprise Data Warehouse (EDW)

EDW is a central warehouse providing decision support services throughout the enterprise. It is utilized when a common approach for managing and arranging data is required. The main benefit of EDW is that it organizes the data according to subject matter and allows access to the sections, which allows for better data management and governance.

Operational Data Store (ODS)

When data warehouses or OLP platforms cannot support your organization’s reporting requirements, ODS is used. It’s best to use ODS for everyday tasks (e.g., document storage for employees) because it permits the database to be updated at a constant rate.

Data Mart

A data mart stands out as unique among the various types of data warehouses. As a variant of a warehouse, a data mart has been specifically designed to be used in specific business segments, such as finance, sales, and so on. Numerous examples exist in which independent data marts allow customers to obtain data straight from their source.

Components Of Data Warehousing

Several key components in data warehouses ensure the system’s smooth operation. Some of the most important components include:

Load Manager

The front end of the data warehousing component is responsible for various operations, including taking data, loading it in the DWH, and converting it into data to be stored within the DWH.

Warehouse Manager

The Data Warehouse Manager handles data management tasks. This module’s primary tasks include creating views and indexes and joining and transforming data sources. Also, analyzing the data to ensure coherence, producing denormalization and aggregations, and archiving and backing up the data.

Query Manager

The query manager functions as the backend of the database warehouse. The name implies that it manages users’ queries, i.e., it permits users to run queries by routing these (queries) towards the correct tables. The degree of difficulty in this part is based on the database’s capabilities and the end-user’s access to the operational tools.

End User Access Tools

Access tools for end users comprise a large part of the DWH. The word “end-user” refers to the various tools users use to access the data contained in the DWH and perform multiple functions involved in data warehousing. Tools are usually classified into five types, i.e.,

  • Tool for querying
  • Tools for reporting data
  • Data mining and OLAP instruments
  • EIS tools
  • Application development tools

Central Database

The core element of a Data Lake Data warehouse is the database. In the past, conventional relational databases were hosted on the cloud or cloud-based. However, due to the rise of the big data era, memory-based databases are now more frequently used. This change results from the demand for real-time performance and a cut in the price of RAM.

Data Integration

A different aspect of a data warehouse is integration, which lets users take data already in the system and then modify it to ensure that the data is aligned for analytic consumption. Various data integration methods are used, including ELT, ETL, bulk loading processing, real-time replication, data quality enrichment, and data transformation.


Metadata, often called “data about the data,” is crucial for data warehouses since it contains the most important information about the information stored. It functions as a catalog or dictionary that outlines aspects like usage, source, and value.

Metadata is classified as business and technical metadata. Technical metadata concentrates on data structure, access, and storage locations, while business metadata details the context. Metadata provides information on the logical structure, records, or indexes and is crucial in handling queries, extraction processing, loading, and other processes.

Source Data

Source data is not surprising to be among the most important parts of the DWH since it is data that is inserted into the DWH. This data is divided into four types: production, internal archived, external, and data that comes from various business operating systems.

However, data such as departmental databases, reports, personal spreadsheets, and customer profiles are called internal data. Additionally, there are different types of information, like archived data, which is the term used to describe older data stored periodically within any operational software’s archived files. External data, as the title suggests, refers to information or data sources originating from outside sources, i.e., outside the company.

Detailed Data

The detailed data supplements the data stored and loaded into the data warehouse. It assists in storing all the information in the database schema.

Summarized Data

Summary data, which includes predetermined aggregates, is an essential part of the data warehouse and is produced by the warehouse administrator to improve information efficiency and accessibility.

Backup Data

The detailed and summarized information is stored and then moved to archives like magnetic or optical disks to ensure backups and archives.

Data Staging

The staged data component is the next, following the data source component. The data needs to be able to be saved once taken from various other sources and operating systems. The primary tasks of this component are data extraction, transformation, and loading.

Many techniques are used when dealing with different types of data sources. However, transforming data involves cleansing, sorting, merging, reshaping, re-duplication, summarization, and data standardization. Additionally, transformed data can perform loading tasks, i.e., moving data to the data warehouse.

Data Storage

Data storage is a vital aspect of a data warehouse. When it comes to data warehouse storage, the majority of data is divided into two repositories. Data repositories are the most structured and regularized data used in operating systems.

Information Delivery

Information delivery is essential to subscribing data to the data warehouse and transferring the files to various locations according to the specifications of the customer schedule method.

Management And Control

The control and management component manages and coordinates the various tasks and functions within the storage facility. It oversees data transformation and its transfer to data warehouse storage.

The program also has additional functions, like providing data to clients using database management, authorizing the data to ensure that it is properly saved at repositories, and tracking the data flow through the staging system before moving it to warehouse storage.

How To Implement Data Warehousing?

Most companies currently use the DWH to save data from various sources and put it in a centralized database. Because the data is stored in one location, companies can easily analyze, report, and gain valuable insights at various levels. To successfully implement a DWH, it is essential to remember that various activities are carried out to gather and distribute information to companies. Here are the most important steps to follow to establish a DWH.

Identify Business Objectives

The initial step in implementing a DWH is identifying your business’s targets. Business analysts prepare a requirements specification. Gathering the required specifications from various clients and stakeholders takes several months. The data modeler identifies dimensions, data, and their combinations based on collected needs. At this point, the company’s demands are identified and placed within this model, which is then used as the DWH’s model.

Data Modeling

The next stage in creating the DWH is to model data. This step displays the data distribution, and then the database is created. The data is then transformed into a format that can be saved within the DWH. Data modeling is just as crucial to making the DWH a blueprint for a house as it is to building an entire home.

Based on the model, data is classified, links between the datasets are made, and data compliance and security standards are set, which is under the DWH’s objectives. Data modeling is the most extensive and complicated phase of DWH implementation because this begins with the data mart and then gradually expands into the DWH. The developer of the data model must evaluate various schemas. This is why data is stored within a data warehouse for different data. Common schemas for developing data models include the star, snowflake, and galaxy schema.

ETL Design And Development

In the final step, ETL (Extract, Transform, and Load) tools allow data extraction from various sources, like data lakes. The ETL process will enable data transformation that meets specifications for data formats and loading information into the DWH for downstream tasks such as reports.

ETL instruments like IBM Information Server, SAS Data Management, and Hive are essential for improving the visualization of data pipelines’ efficiency, speed, and coherence between the brand-new data warehouse and the old infrastructure. They guarantee effective ETL processes and help rapidly build an effective data warehouse at every organizational level.

OLAP Cubes

When data is changed into a new format, it could comprise non-related but crucial performance indicators covering different business elements. To monitor various aspects of business operations, it is essential to recognize those involved in generating the indicators. The data structure is required to allow for quick analysis across multiple dimensions.

That’s where an OLAP cube, a hypercube or multidimensional cube, becomes crucial. A data warehouse typically takes information from various sources in different formats (such as Excel sheets, media files, text files, etc.), cleanses it, and normalizes it. Then, the data is loaded into the OLAP cube (or OLAP server) for further investigation. In contrast to two-dimensional data organized into columns and rows in a spreadsheet, the data contained in a DWH comes from various sources and formats. An OLAP cube assists in structuring and logically storing multidimensional data.

Development Of UI

At this point, the main focus was developing backend functionality. The user interface is currently designed to allow users to interact with computers. Users use input software to interact with the data warehouse, using various UI development tools, BI tools, and data analysis tools.


Monitoring any modifications made to the DWH schema, domain of application, or requirements is crucial. Thus, implementing the maintenance of data warehouses will be essential in observing any changes or modifications to the system. It includes changes to dimensions and data categories and the insertion or elimination of attributes defined by users. 

Test And Deployment

The final step in developing and implementing the DWH is testing thoroughly to verify that the DWH can meet the company’s targets. Various steps can be performed during testing, including data quality and integrity, data integrity, data transformation, the effective use of ETL tools, etc. Following the testing, you can deploy the newly developed DWH to ensure users can access it and conduct the required analysis. 

The DWH can be used in your company’s cloud systems. When all the functions have been implemented and verified as operational, the data warehouse implementation will likely be a success.

Examples Of Data Warehousing

Data warehouses are now the norm for businesses as they are an effective method of storing and processing information. Here, we will discuss a few industries that cannot manage their day-to-day operations without data warehouses.

Investment And Insurance Area

Data Warehouses can provide instant analysis of data as well as their patterns. The stock market and financial markets cannot function without DWHs. The DWH can primarily assess market and customer changes and other statistical assets. Two of the most essential sub-sectors in the financial market, the stock market and forex, rely heavily on DWHS because a single error can lead to massive losses for the entire panel. The DWH emphasizes live data broadcasting in these markets, which gives more insight into market trends.

Retail Chains

Data warehousing is widely used in retail businesses to monitor goods, analyze the cost structure, monitor advertisements, and study buyer behavior. Commercial organizations typically employ EDW platforms to meet business analytics and forecast targets.


Data warehouses are also used in healthcare to evaluate and predict outcomes and create patient record records. Also used to transfer data to interlinked health insurance companies, medical support service providers, and other organizations.

Public Sector

For the public sector, data warehouses can be found to accumulate, store, and process vast quantities of government data. The government uses data warehouses to manage tax information, health records, demographics, and other crucial data sets essential to policymaking in resource allocation and compliance with regulations. Through centralizing information storage and analysis, data warehouses allow authorities to make decisions on data, increase transparency, and enhance the quality of services provided to citizens.


The increasing volume of data companies produce has led them to concentrate on data warehouses. Data warehouses are a fantastic option for handling today’s huge quantities and varieties of information. Today, companies expect that you are aware of the diverse elements of data warehouses, such as their kinds of components, their types, and their development phases, which are discussed in this piece. However, examining the various types of schemas,

Data Lake Architecture, and warehouse architecture is important. Common architectures include host-based, network-based multi-stage, single-stage, distributed, and digital data warehouses. Expanding beyond conventional databases to data warehousing could help businesses get more out of their research and analysis. The right warehouse solutions for businesses could significantly impact how you service customers and increase the scope of your operations.


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