Data Warehouse And Its Architecture

Data Warehouse and its architecture

What Is Data Warehouse

A data warehouse is a Subject-Oriented, Integrated, Time-Variant, and Non-Volatile collection of data in support of management’s decision-making process.

It is also defined in many ways:-
  • A decision support database that is maintained individually from the organization’s operational database.
  • Supports information processing by providing a solid platform of consolidated, historical data for analysis. 


It's organized around major subjects, such as customers, products, sales.

It focuses on the modeling and analysis of data for decision-makers, not on daily operations or transaction processing.

It provides a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process.


It is constructed by integrating multiple, heterogeneous data sources.
  • Relational databases, flat files, on-line transaction records
Data cleaning and data integration techniques are applied.
  • Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources
  • E.g., Hotel price: currency, tax, breakfast covered, etc.
  • When data is moved to the warehouse, it is converted.  


The time horizon for the data warehouse is significantly longer than that of operational systems
  • Operational database: It operates on current value data
  • Data warehouse data: It provides information from a historical perspective (e.g., past 5-10 years)
Every key structure in the data warehouse,
  • It contains an element of time, explicitly or implicitly.
  • But the key to operational data may or may not contain “time element”. 


A physically separate store of data transformed from the operational environment.

The operational update of data does not occur in the data warehouse environment.

  • Requires only two operations in data accessing:
  • Initial loading of data and access to data.

Heterogeneous DB vs Data Warehouse

Traditional heterogeneous DB integration: A query-driven approach
  • Build wrappers/mediators on top of heterogeneous databases.
  • When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set.
  • Complex information filtering, compete for resources.

Data warehouse: It is of update-driven and high performance
  • Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis.

Operational Database

An operational database is a database that is accessed and updated continually and usually handles the daily transactions for a business. Operational databases use an OLTP approach and are designed to be write-optimized.

Why Separate Data Warehouse?

High performance for both systems
  • DBMS— It is tuned for OLTP: access methods, indexing, concurrency control, recovery
Different functions and different data:
  • Missing Data: Decision support requires historical data which operational DBs do not typically maintain.
  • Data Consolidation:  Decision support requires consolidation (aggregation, summarization) of data from heterogeneous sources.
  • Data Quality: Different sources typically use inconsistent data representations, codes, and formats that have to be reconciled.

Note: There are more and more systems that perform OLAP analysis directly on relational databases.

Data Warehouse Architecture

Data Warehouse Architecture

Business Analysis Framework

Steps for its Design
First, having a data warehouse may provide a competitive advantage by presenting relevant information from which to measure performance and make critical adjustments to help win over competitors.

Second, a data warehouse can enhance business productivity because it can quickly and efficiently gather the information that accurately describes the organization.

Third, a data warehouse facilitates customer relationship management because it provides a consistent view of customers and items across all lines of businesses, all departments, and all markets.

Finally, a data warehouse may bring about the cost reduction by tracking trends, patterns, and exceptions over long periods consistently and reliably.

To design an effective data warehouse we need to understand and analyze business needs and construct a business analysis framework.

The construction of a large and complex information system can be viewed as the construction of a large and complex building, for which the owner, architect, and builder have different views.

These views are combined to form a complex framework that represents the top-down, business-driven, or owner’s perspective, as well as the bottom-up, builder-driven, or implementor’s view of the information system.

There are 4 views regarding the design of a data warehouse

Top-down view
  • It allows the selection of the relevant information necessary for the data warehouse. This information matches the current and future business needs.
Data source view
  • It exposes the information being captured, stored, and managed by operational systems.
Data warehouse view
  • It consists of fact tables and dimension tables.
Business query view
  • It sees the perspectives of data in the warehouse from the view of the end-user.

Building and using a data warehouse is a complex task because it requires business skills, technology skills, and program management skills.

Business skills
  • How to build extractors that transfer data from the operational system to the data warehouse?
  • How to build warehouse refresh software that keeps the data warehouse reasonably up-to-date with the operating system’s data?
  • Understanding and translating the business requirements into queries that can be satisfied by the data warehouse.

Technology skills
  • Data analysts are required to understand how to make assessments from quantitative information and derive facts based on conclusions from historical information in the data warehouse.

Program management skills
  • Involve the need to interface with many technologies, vendors, and end-users in order to deliver results in a timely and cost-effective manner.

Types Of Data Warehouse Models

Enterprise Warehouse
  • Collects all of the information about subjects spanning the entire organization.
Data Mart
  • A subset of corporate-wide data that is of value to specific groups of users.  Its scope is confined to specific, selected groups, such as marketing data mart.
  • Independent vs. dependent (directly from the warehouse) data mart.
Virtual Warehouse
  • It is a set of views over operational databases
  • Only some of the possible summary views may be materialized


A data warehouse is a Subject-Oriented, Integrated, Time-Variant, and Non-Volatile collection of data in support of management’s decision-making process.

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