What is a Data Warehouse? Introduction, Features and Forms: In layman terms, a data warehouse would mean a huge repository of organized and potentially useful data. This is what Bill Inmon, the person who coined the term itself, had in mind when he introduced data warehouses to the world of Information Technology in 1990.According to the man himself, a data warehouse is a clear, integrated representation of a non-volatile collection of both historical and current data. Analysis of this data helps organizations take informed decisions.

 

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DATA WAREHOUSE

Also See: Data Warehouse Architecture

What is a Data Warehouse? Introduction, Features and Forms

Understanding a Data Warehouse

One of the primary aspects of databases is that they are constantly updated, while few of them are updated every second, few of them undergo frequent changes on a daily basis. Let’s take the example of a small business where their transactions are updated every week. Problems arise when an executive feels the need to revert back to previous data of a false transaction or any consumer data, as the executive will be unable to access previous data as it was updated.

Data warehouses serve to tackle this issue as they present a multidimensional view of consolidated data and are also equipped with online analytical processing (OLAP) tools. Through these tools, detailed analysis of the consolidated data can be generated thereby generalizing it and recovering previous data, two features which are unique to data warehouses. Apart from recovering data, several other data mining functions, like classification, association, prediction, and clustering of data can be practiced through data warehouses which expands the playfield for multiple levels of abstraction. The ability to operate at a high degree of data convolution was the gateway for data warehouses as an integral component of data analysis and online analytical processing.

Also See: 9 Disadvantages and Limitations of Data Warehouse

Features of a Data Warehouse

  • Subject Oriented– One of the key features of a data warehouse is the orientation it follows. Data warehouses focus on past subjects, like for example, sales, revenue, and not on ongoing and current organization data. This enables it to be used for data analysis which is a key element of decision-making.
  • Collaboration – Adding on as another feature for ease of analysis of data, a data warehouse’s core is its integration of data from several different sources which aren’t homologous in nature, for example, flat files, relational databases, and other such sources. This plays a key role in enhancing the efficacy of data analysis.
  • Non-volatile–The data in a warehouse is of the non-volatile type which ensures that your previous data is not lost as new data is updated which separates them for operational databases which are subject to frequent changes.
  • Time Variant –What’s the significance of data without a time stamp? Data uploaded into a warehouse can be identified with a certain timeline making it a multidimensional historical view whenever you access data.
  • No Additional Controls – As the warehouse is maintained separate and has a separate storage from the operational databases, it doesn’t require any concurrency controls, tweaks in processing, recovery mechanisms.

Different Forms of a Data Warehouse

Based on their application, there exist three different types of data warehouses. These types focus on a specific property of warehouses and are exclusively used for the same. The three types are as follows:

  • Information Processing Data Warehouses–These specifically allow processing of historical data which is stored in it. There are several processing operations which can be performed, like for example, query, generating tables, charts, or graphs, and basic operations under statistical analysis
  • Analytical Processing Data Warehouses– These warehouses can be used for extensive analytical processing. This analysis is performed on the data which is stored in the warehouse by performing several OLAP operations, and few others like pivoting, slice-and-dice, drill down and drill up which enhance the results of the analysis.
  • Data Mining Data Warehouses– These warehouses are dedicated to data mining, the discovery of information by uncovering hidden patterns, prediction techniques, and analytical model construction.

Also See: Applications of Data Warehouse

What Separates a Data Warehouse from a Traditional and Operational Database

A data warehouse is essentially a database. However, it is kept separate from an organization’s operational databases because of the following reasons:

  • The operations that a data warehouse can fulfill are much more complicated when compared to an operational database which, for example, is used to perform tasks such as searching particular records which are simpler. In contrast, the queries for a data warehouse are often complex and deal with a large amount of data.
  • Operational database queries aren’t just read-only as they have to be equipped with operations for modifying data. However, an OLAP query in a data warehouse requires only read-only access to stored data.
  • Often operational databases have concurrency support and recovery mechanism set up for they are able to handle multiple transactions.

 

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DATA WAREHOUSE

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