What Is a Data Warehouse? 3 Types of Data Warehouses

Shriya Sarang

20th Nov'22
What Is a Data Warehouse? 3 Types of Data Warehouses | OpenGrowth

A data warehousing (DW) process is used to gather and manage data from many sources in order to produce insightful business information. Business data from many sources is often connected and analyzed using a data warehouse. The central component of the BI system, which is designed for data analysis and reporting, is the data warehouse.

The strategic use of data is facilitated by the integration of numerous technologies and components. A corporation stores a lot of data electronically with the intention of using it for analysis and research rather than transaction processing. It involves transforming data into information and quickly making it available to users so that it can have an impact.

The operational database of the organization is kept distinct from the decision support database (Data Warehouse). The data warehouse, on the other hand, is an environment rather than a product. It is an architectural design element of an information system that gives users access to recent and historical decision-support data that is not readily available or accessible in the conventional operational data store.


History of Datawarehouse


Users can better analyse and improve their organization's performance with the help of the data warehouse. As computer systems grew more complicated and needed to process larger volumes of information, the requirement to store data changed. Data warehousing is not a new concept, though.

Here are some significant occasions in the development of data warehouses:


  • The phrases dimensions and facts are created in 1960 as a result of a cooperative research initiative between Dartmouth and General Mills.

  • Dimensional data marts are first introduced for retail sales in 1970 by A Nielsen and IRI.

  • Tera Data Corporation debuts a database management system in 1983 that is intended solely for decision assistance.

  • Paul Murphy and Barry Devlin, two IBM employees, created the Business Data Warehouse in the late 1980s, which marked the beginning of data warehousing.

  • However, Inmon Bill provided the actual notion. He was revered as the inventor of the data warehouse. He had written on a range of subjects related to the construction, operation, and upkeep of the warehouse and the Corporate Information Factory.


How does Datawarehouse operate?



One or more data sources send information to a data warehouse, which acts as a central store for that information. The transactional system and other relational databases feed data into a data warehouse.


Data could be:


  • Structured

  • Semi-structured

  • ad hoc information


Users can access the transformed data in the Data Warehouse through Business Intelligence tools, SQL clients, and spreadsheets after the data has been changed, transformed, and ingested. A data warehouse compiles information from various sources into a single thorough database.

An organisation can examine its clients more thoroughly by combining all of this data in one location. This makes sure that all the information is taken into account. Data mining is made possible by data warehousing. Data mining searches for patterns in the data that could result in increased revenue and profitability.


Which people need data warehouses?


All different categories of users, including:


  • Those who make decisions based on a lot of data

  • Users who collect information from numerous data sources using complex, specialised techniques.

  • People that prefer straightforward technologies to get info also use it.

  • It is also crucial for individuals who want to make decisions in a methodical manner.

  • Data warehouses are helpful if the user needs quick performance on a large amount of data that is required for reports, grids, or charts.

  • A first step is a data warehouse. if you're looking for "hidden patterns" in data flows and groupings.


Data warehouse types


Enterprise data warehouses (EDW), operational data stores (ODS), and data marts are the three primary forms of data warehouses. Read about how you can use virtual reality in retail business here


1. Business Data Warehouse (EDW)


A centralised warehouse called an enterprise data warehouse (EDW) offers decision support services to the entire organisation. EDWs are often made up of a number of databases that provide a consistent method for classifying data and arranging data by subject.


2. Operational Data Store (ODS)


The enterprise data warehouse previously mentioned uses an operational data store (ODS), which is a central database utilised for operational reporting. An ODS is utilised for operational reporting, controls, and decision-making and is a complementing component of an EDW.

An ODS is suitable for routine tasks like keeping employee records because it is refreshed in real-time. On the other side, tactical and strategic decision assistance are provided by an EDW.


3. Data Mart


A data mart is regarded as a subset of a data warehouse and is typically targeted at a particular team or business line, like finance or sales. It is subject-oriented, making particular data more quickly available to a specified group of users and giving them crucial insights. They won't have to waste time looking through a large data warehouse because that information is readily available.


Data warehouse advantages


The main advantage of a data warehouse is the ability to store, analyse, and derive value from massive quantities of diverse data while retaining past data for record-keeping.

The inventor of data warehousing, Bill Inmon, listed four distinctive qualities of data warehouses, including:


  • subject-oriented, with an emphasis on a specific area;

  • the capacity to combine multiple data kinds from various sources;

  • stable (non-volatile); and

  • Time-variant analysis tracks alterations over time.


Potential Drawbacks of Using a Data Warehouse


Although they have their limits, data warehouses are useful for many firms. Data warehouses perform best when processing data of a narrowly defined type. By efficiently and consistently carrying out particular data analysis tasks, they maximise speed. Broader and more diverse forms of data will be needed by more and more businesses, and traditional data analytics are insufficient.


The Conclusion


Well-designed data warehouses produce high-quality data and quickly respond to queries. It enables end users to scale back the amount of info so they can focus on a specific region. Data warehouses are essential for making quicker decisions because they give data in uniform formats that are ready for analysis.

Additionally, data warehouses give users access to a comprehensive dataset and analytical power to enable data-driven decision-making based on high-quality information from all business domains.

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