What is Data Warehousing?

Data warehousing can be defined as a particular area of comfort wherein subject-oriented, non-volatile collection of data is done as to support the management’s process.

It senses the limited data within the multiple data resources.

It has built-in data resources that are modulated upon the data transaction.

Data Warehousing Pros Cons

Characteristics of Data Warehouse:

Data warehouse can be modulated when the people has a common way of explaining new things that are emerged as particular subject. Here are some of the few characteristics of data warehousing.


It can be performed on a particular subject area. It means the data warehousing process is intended to deal with a particular subject that is more defined.

A deep understanding will help in developing sales procedures that are defined within the bounds. It deals with all the subject matters that have a warehouse.


It discovers different time limits that are modulated within the large amounts of data and are held in online transaction processing.

It is meant by time-variant when the data is set into the causes of the support of staging files.

It normally proceeds with the majority of data that is handled by large tables containing updated facts.


It encompasses the high quantity of data that enters into change within the selected quantity on logical business. It enumerates the analysis in the warehouse technologies.

Non-volatility will make people understand what has occurred. It makes a clear sense of analysis that is done.


It is similar to subject orientation that is made in a consistent format. It should resolve the problems and are made to be disparate problem. It has a finite number of procedures for the issues such as naming conventions, conflicts, units of measure, inconsistent values. It manages different subject related warehouse information.

Functions of Data Warehouse:

It works as a repository and the data here is held by an organization that endures the facilities to backup data functions.

It reduces the cost of the storage system and even the backup data at the organizational level.

It has stored facts about the tables that have high granular transaction levels that are monitored so as to define the data warehousing techniques. Functions involved are:

  • Data consolidations
  • Data cleaning
  • Data integration
  • Data Extraction
  • Data Cleaning
  • Data Transformation
  • Data Loading
  • Refreshing

Alternative Names for Data warehouse system:

Data warehouse system is also known by the following names,

  • Decision Support System (DSS)
  • Executive Information System
  • Management Information System
  • Business Intelligence Solution
  • Analytic Application
  • Data Warehouse

How Data Warehouse Works?

A data warehouse is a place where data is collected by the information which is flown from different sources. Usually, the data is passed through relational databases and transactional systems. The data from here can be assessed by users as per the requirement with the help of various business tools, SQL clients, spreadsheets etc.

The data flown will be in the following formats

  • Structured
  • Semi-structured
  • Unstructured data

Types of Data Warehouse:

There are mainly 3 types of Data Warehouses, and they are

  1. Enterprise Data Warehouse
  2. Operational Data Store
  3. Data Mart

Data Warehouse Stages :

The usage of data warehousing was basically simple earlier, but as time passed by the procedures in assessing the data has changed a lot. Following are the few stages involved in the use of data warehousing

  • Offline Operational Database
  • Offline Data Warehouse:
  • Real-time Data Warehouse:
  • Integrated Data Warehouse

Components of Data Warehouse:

The 4 different components of data warehousing are,

  1. Load manager
  2. Warehouse Manager
  3. Query Manager
  4. End-user access tools

Applications of Data Warehouse:

The business executives help in performing various other businesses to organize and analyze the detailed data description. These instances are executed within the loop and are monitored within a closed loop. Data warehousing is mainly followed in the following fields:

  • Airline
  • Banking services
  • Healthcare
  • Public sector
  • Investment and Insurance sector
  • Telecommunication
  • Hospitality Industry
  • Financial services
  • Retail sectors
  • Consumer goods
  • Controlled manufacturing

Steps to Implement Data Warehouse:

The risk connected to data warehousing implementation is huge and need to to be taken into consideration at the earliest and the finest way is to use a 3 level strategy.

  • Enterprise strategy
  • Phased delivery
  • Iterative Prototyping

Here are a few steps in the implementation of Datawarehousing along with its deliverables.

Datawarehouse Implementation Table
Steps Tasks Output
1 Specifying project scope Scope definition
2 Ascertain business needs Logical data model
3 Defining Operational Datastore requirements Operational Data Store Model
4 Develop or Obtain Extraction tools Extract software and tools
5 Specifying Data Warehouse Data Needs Transition Data Model
6 Document missing information To Do Project List
7 Mapping Operational Data Store to Data Warehouse D/W Data Integration Map
8 Improve Data Warehouse Database design D/W Database Design
9 Pull Out Data from Operational Data Store Integrated D/W Data Extracts
10 Load Data Warehouse Initial Data Load
11 Manage Data Warehouse Continuous Data Access and Subsequent Loads

Data Warehouse Tools:

Though you can find many data warehouse tools online, we have mentioned here few best ones

Pros or Advantages of Data Warehousing:

It is a common process for the new implementations in a business that are based on various intelligent plans. Out of the many advantages of data warehousing, some of them are discussed below.

a. Cleans data:

It is mainly done in data cleansing of removing errors that are inconsistent to improve the data and its respective quality. It emerges as a database containing many files. It has variety of resources that are made by using creativeness. It undergoes a process that enables one to deal with data cleaning substances.

Metadata is reflected in sufficient quantity that is especially meant for all the constraints and even the system translation.

b. Indexes multiple types:

Indexing has created multiple database tables and created to speed up the accessing of information. It has huge number of existence that is modulated within the desired scenario.

It can handle large quantity of data and iterative queries before building the aligned form of data using OLTP applications. It has huge number of existence within the modulated database system management queries.

c. Secured data and its access:

Security is the best way to mitigate the self published breaches on rapid warehousing and that has to be applied for all aspects as tradeoffs into potential warehousing behavior.

It has consolidated layered form of data with the objectives enabled and database enforced as to improve its values and gains.

It has critical compromising of sequential data within the unauthorized access.

d. Query processing with multiple options:

Query processes are carried out in a parallel manner that helps in defining the unthinkable state of technology. These query tools are designed to process and load the data into various modules.

It is accessed using simple logic’s along with parallel repository of data. It enhances the defined field of routes and queries. It has large number of query tools that are managed with the heterogeneous resources. It handles requests from the tools online.

e. Enhanced business intelligence:

These insights are developed within the information access and are freed from decision making. It limits the gut feelings and also defines each strategic credible fact of the evidence and backup.

It has personal needs that are varied within the better involved decision makings that are more competent with that of the limited data. It has warehouse related business tactics that are measured within the informed facts. Financial management plays a vital role within the inventory management.

f. Increased system and query performance:

It is mainly constructed to enhance and find the retrieval of data. It has speed of performing different warehouse and the corresponding storage on large volumes. It has credible facts that are involved for storing large values.

It enables within the sequential information mediated within the business intelligence and has defined the modules that are matched with the personal needs. It is constructed with the operations of multiple subsystems. It concludes business intelligence and to alleviate the business repository. It gathers efforts for extracting the information.

g. Business Intelligence:

Many enterprises form a detailed log of multiple subsystems. It has different platforms that are physically built within the data sources and are accessible to a single phase of data.

It has defined platforms that made different multiple sources and are imagined to have a consolidated enterprise.

It enables a single data repository on a detailed subject as to ensure that there is no duplication of data.

h. Timely access to data:

It helps the users to access different resources to analyze the data for the retrieval process. It spends time on scheduled information of data that are sequenced into routines. It has multiple resources that hold time for information technology.

It sustains for the queries and the consuming of data on query language. It has lesser information about the ability to generate standard reports that are defined with special performance. It also has professional queries that are diminished against warehouse reports.

i. Enhanced data consistency and quality:

It manages and sequences the illuminated data with standardization of unique system resources. It has individual sales and utilization of repository of data.

It has different and consistent units of substantially increased business. It accounts to the repository of operations and manages the unique resources.

j. Return on investment is high:

Here the ROI is made as a revenue part and with decreased expenses. It is a business that is enabled to realize the project capital within the generated revenues and the cost savings.

The study of the business and substantial impact upon the analytics of the financial status can be divided into various business studies.

k. Increase revenues:

It manages similar investigation systems that are joined up for approach that might link to the stability of work and are modulated within the deployed data on the database. It exists among the isolated warehouse departing from the cross checks and are managed with central point of each database.

It also follows proactive approach within the linked database to detect and prevent the summarized reports. It proactively minimizes the corporate investigators that are matched with increased streams.

l. Standardizes data across organization:

Data standards are followed on different secured sharing of data. It has particular standard within the modulated and visualized knowledge about connectivity. It contributes to numerous applications and are organized within the delivered data management systems. Conflict between data sharing is avoided. It has critical applications that are sequenced.

m. Database normalization:

The data can be stored and extracted in various forms that are stored in warehouse reports. It is a process of organizing the data in relational database to minimize redundancy and that are more helpful in organizing the data. It emerges as a sequential flow of all the required data that are minimized.

Cons or Disadvantages of Data Warehousing:

Even though there are a lot of advantages, people involve in implementing time and cost with high sequences that involves data translation, long time implementation of processes, lack of flexibility in the data transferred. Here are some of the disadvantages of data warehousing explained:

a. Raising ownership:

Majority of the data that are passed are held from the data resources and are represented within multiple efforts of data warehouse. It intimates long term implementation of the schema and its resources. It has its own issues with raising ownership, privacy and secured results. It is associated with long term owners and with high costs.

b. Extra reporting:

The data warehouse will be run depending on the risks of the organization. It has typical generated teams those help in business negotiations. It manages to duplicate the data existence within the sequencing of long term database. It consumes more time when the extra reporting is done.

c. Data flexibility:

It is arranged when the data that is imported have many static complaints and abilities that are mapped with the same schema and enumerated filtered displays. It is often recognized leaks between customers of an organization.

It generates analysis reports within the related privacy of the customer and is defined with minimal ability. It has limited value and constant transition that are mapped within the sequential processing of data.

d. Compatibility with existing system:

Data warehouse system can be managed within the regular extract of the data that are loaded into the system. The usage of technology requires modification of data that has foremost concerns. All the existing system functionalities that are engaged are considered to be complex.

e. Keeping data online:

Softwares do not allow keeping the entire repository online after certain duration. It maintains the data online and is enlarged by its textual means and large data online. It records and analyses the data for future reference.

f. Dimensional technique:

This technique contains all the information with specific events. It has limited amount of information that is identified with the proper understanding of all the events. It is used for many of the practical applications that are redundant. The process of updation, deletion and insertion are processed here. It accounts to the detailed description of undesirable characteristics of data warehousing.

g. Costs:

Nowadays maximum of the business started using techniques of data warehouse. So the price range has fallen under the price range that most of the products towards are designed.

It is complicated because even the small business details are formed when the situations are capable of designing the data provided. It manages the price range between the people among the company.

Thus, most of the tools that are used begin with the transactions which incase accounts to the techniques of data warehousing. It groups all the transactions and signifies each and every operation that is reported in detail. It can access large amount of information and will enable neutral network that is replaced with the warehouse. Users are supposed to be trained before using warehouse techniques.