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Multi-tier architecture of Data Warehouse

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  • Difficulty Level : Medium
  • Last Updated : 02 Mar, 2021
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A data warehouse is Representable by data integration from multiple heterogeneous sources. It was defined by Bill Inmon in 1990. The data warehouse is an integrated, subject-oriented, time-variant, and non-volatile collection of data. A Data Warehouse is structured by data integration from multiple heterogeneous sources. It is a system used for data analysis and reporting. A data warehouse is deliberate a core factor of business intelligence. BI technology provides a historical, current, and predictive view of business operations without data mining many businesses may not be able to perform effective market analysis, the strength and weakness of their competitors, profitable decisions, etc.

Data Warehouse is referred to the data repository that is maintained separately from the organization’s operational data. Multi-Tier Data Warehouse Architecture consists of the following components: 

  1. Bottom Tier
  2. Middle Tier
  3. Top Tier

Three/Multi-tier Architecture of Data Warehouse

Bottom Tier(Data sources and data storage) :

  1. The bottom Tier usually consists of Data Sources and Data Storage. 
  2. It is a warehouse database server. For Example RDBMS.
  3.  In Bottom Tier, using the application program interface(called gateways), data is extracted from operational and external sources. 
  4. Application Program Interface likes ODBC(Open Database Connection), OLE-DB(Open-Linking and Embedding for Database), JDBC(Java Database Connection) is supported.

Middle Tier :

The middle tier is an OLAP server that is typically implemented using either : 
A relational OLAP (ROLAP) model (i.e., an extended relational DBMS that maps operations from standard data to standard data); or A multidimensional OLAP (MOLAP) model (ie, a special purpose server that directly implements multidimensional data and operations).

Top Tier :

The top tier is a front-end client layer, which includes query and reporting tools, analysis tools, and/or data mining tools (eg, trend analysis, prediction, etc.).

Data Warehouse Models :

From the architecture point of view, there are three warehouse models-

Enterprise Warehouse:- 

  • An enterprise warehouse collects all information topics spread throughout the organization. 
  • It provides corporate-wide data integration, typically from one or several operational systems or external information providers, and is cross-functional in scope. 
  • It usually contains detailed data as well as summarized data and can range in size from a few gigabytes to hundreds of gigabytes, terabytes, or beyond. Can be an enterprise data warehouse. 
  • The traditional mainframe, computer super server, or parallel architecture has been implemented on platforms. This requires extensive commercial modeling and may take years to design and manufacture.

Data Mart:- 

  • A data mart contains a subset of corporate-wide data that is important to a specific group of users. 
  • The scope is limited to specific selected subjects. 
  • For example, a marketing data mart may limit its topics to customers, goods, and sales. 
  • The data contained in the data warts are summarized. Data warts are typically applied to low-cost departmental servers that are Unix/Linux or Windows-based. 
  • The implementation cycle of a data mart is more likely to be measured in weeks rather than months or years. However, it can be in the long run, complex integration is involved in its design and planning were not enterprise-wide.

Virtual Warehouse:- 

  • A virtual warehouse is a group of views on an operational database. 
  • For efficient query processing, only a few possible summary views can be physical. 
  • Creating a virtual warehouse is easy, but requires additional capacity on operational database servers.
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