It is a centralized location where the data from several sources are integrated. The data gathered here is used in several combinations from different business streams for improved planning and critical business decisions.

Data Warehouse

Where is it used?

You know that a data warehouse is a core component of Business intelligence. It is also called an Enterprise data warehouse (EDW).

It is used for reporting and analysis. It stores old data and also uses real-time data to generate business reports.

Below are the familiar sectors where the data warehouse is used.

  1. Public region:  In this area, the data warehouse is used for collecting intelligence in government offices. It is also used for monitoring and analyzing the health records, tax records of each individual in government offices.
  2. Bank sector: It helps the banking sector to control and investigate the available resources on desks.
  3. Hospitality Industries like hotels, and restaurants:  In this sector, data warehouse helps to promote themselves and to attract target customers.
  4. Health care: In this area, the warehouse helps to generate patient treatment reports.
  5. Airlines: Here, warehouse is used for analyzing the works assigned to the airline crew.
  6. Insurance: In this sector, the warehouse helps to trace the market fluctuations.

How can a data warehouse benefit an organization?

1. Subject-oriented

A specific business purpose can be analyzed with the data collected from here.

Suppose the business wants to understand the machine downtime and how it can reduce. In that case, data can be collected from the data warehouse to understand the various times or situations during which the machines stopped working, the reasons behind the same, and how this can be reduced.

2. Integrated

Data from different sources are integrated to provide collective data. For instance, if a company wants to do budgeting for the next quarter, a data warehouse will have all the information required.

From incurred costs to depreciation costs, the entire set of data is available in one single source.

3. Time-variant

The company utilizes the historical data stored in the system to extract relevant reports and understand the overall organization’s health.

But data such as the employee database, which includes addresses and phone numbers, must not be included as they are subject to change.

4. Non-volatile

Once data is entered, it remains the same. Therefore, the firm must ensure that information is highly protected, and there is no change for alteration.

If there are any modifications made, then it will affect the reports and analysis.

5. Improved data quality

Helps to improve data quality by providing consistent, accurate data and fixing insufficient data.

Disadvantages of data warehouse

Cost v/s Benefit

A data warehouse is an IT project, and it consumes more man-hours and more money from the budget. Moreover, its implementation and maintenance are very expensive.

Hence the cost to benefit ratio is very low. However, if the organization is small and medium, it may affect the revenue of the organization.

Data Ownership

We know that data warehouses are software applications for service. The main concern of it is the security of data.

You have to be more sure that the people who handle and analyze the customer data are the employees that your company trusts.

Because leaking of the customer’s personal data within the organization may cause problems for executives and also affect the relationship between the company and the customer.

Data Rigidity

The data that is imported into the data warehouse is often static data sets that have less flexibility. They have less ability to generate a particular solution.

Warehouses are subjected to ad hoc queries that are highly difficult due to their most minor processing and query speed.

Miscalculation of ETL processing time

The entire process of data warehouse development is extraction, cleaning, and loading of consolidated data into the warehouse takes more time.

But usually, organizations do not guess the time required for the ETL process. As a result, it leads to a backlog of works in the organization.

Levels of data warehouse architecure

It comprises several levels. A few of them are as mentioned below:

  • Data Source Layer
  • Data Extraction Layer
  • Staging Area
  • ETL Layer
  • Data Storage Layer
  • Data Logic Layer
  • Data Presentation Layer
  • Metadata Layer
  • System Operations Layer
Architecture of Data Warehouse

Types of data warehouse architecture

Mainly three types

Single tier architecture: It is rarely used architecture. It reduces the amount of data stored by avoiding repetition.

In this type of architecture, only the source layer is available. Thus, the single-tier consists of the source layer, data warehouse layer, and analysis layer.

Two-tier architecture: It consists of a data staging area or ETL (extraction, transformation, and loading) and the source layer.

This layer helps to merge diversified data into one standard schema. This type of architecture consists of the source layer, data staging layer, data warehouse layer, and analysis layer.

Three-tier architecture: In this architecture contains reconciled layer along with the data staging and source layer.

The source layer contains multiple sources in this architecture, and the data warehouse layer has data warehouses and data marts.

The role of a reconciled layer is to generate a standard data model for the entire enterprise. This reconciled layer can also use to do some operational works like reporting.

This architecture consists of the source, data staging, reconciled, data warehouse, and analysis layers.

Types of data warehouse

The following three are the main types of the data warehouse.

1. Enterprise Data Warehouse (EDW): It helps to provide decision support service throughout the enterprise and also helps to classify data according to the subject.

2. Operational Data Store: It helps to store records of employees.

3. Data Mart: It helps to collect data directly from sources.

Data Warehouse Types

Data warehouse tools

Following are the few popular tools of data warehouse

  • QuerySurge
  • Oracle
  • Amazon Redshift
  • Microsoft Azure
  • Panoply
  • Xplenty
  • CData Sync
  • Domo
  • Snowflake
  • SAP HANA
  • Teradata
  • SAS
  • MarkLogic 
  • Amazon RDS
  • Amazon S3
  • Maria DB
  • Exadata
  • Cloudera

Difference between database(DB) and data warehouse(DW)

Many people get confused between these two concepts. So here I am going to state the differences.

  • DW transfers and stores accumulated data for analytical purposes. Whereas DB collects data for multiple transactions.
  • DW developed for accumulation and recapture of the large data sets. But DB developed for write or read access.
  • DW made for easier analysis of data collected and stored from multiple databases. DB made for quick record and recapture data.

Data warehouse history

In the 1950s American government and businesses started using punch cards to store computer-generated data. They were being used till the 1980s.

In the 1960s, slowly disk storage systems came into the picture, and in 1964 the systems became popular, called ‘magnetic storage’ for data.

IBM is the first company that designed and started using the floppy disk drive. Later is called the hard disk drive.

In 1966, IBM designed its DBMS(database management system) called ‘information management system’. It contained the following features.

  • Ability to find out the exact location of data
  • Ability to solve the problem of locating more than one unit of data in the same place
  • Ability to delete data
  • Ability to access the data rapidly
  • Ability to allocate the place when data stored is not able to fit in the specified place

In 1970, online applications came into the picture. People come to know that data can be directly accessible and shared between computers.

After that, people started using their personal computers. It changed the way of doing work. At the same time, 4GL technology has been invented.

The combination of personal computers and 4GL technology gave complete freedom to the end-user. It allows end-users to access their data efficiently and rapidly by providing control over the computer system. But they found the following problems.

  • They got mislead by incorrect data
  • Old data is not at all useful
  • Got confused because of duplicated data

As a solution to these problems rational database being used in the 1980s. It used SQL (structured query language)as its language.

Businesses started assigning personal computers to the employees and widely used office applications( ms word, ms excel, ms office).

In the year 1990, significant changes took place. That is the usage of the internet. Internet became very popular, and conflict started because of globalization, computerization, and networking.

During 2000, businesses needed good integration between systems and consistent data to get accurate business information required for proper decision-making.

Because of expanded databases and application systems, getting consistent data became difficult. To fulfill these needs data warehouse is developed by businesses.

Get more definitions about data warehouse and other ERP-related terms here.

   

Subscribe to our Email Newsletter