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Data warehouse
From Wikipedia, the free encyclopedia
Data Warehouse Overview
Incomputing, adata warehouse(DWorDWH), also known as anenterprise data 
warehouse(EDW), is a system used forreportinganddata analysis. DWs are central 
repositories of integrated data from one or more disparate sources. They store current 
and historical data and are used for creating trending reports for senior management 
reporting such as annual and quarterly comparisons.
The data stored in the warehouse isuploadedfrom the operational systems (such as 
marketing, sales, etc., shown in the figure to the right). The data may pass through 
anoperational data storefor additional operations before it is used in the DW for 
reporting.
Contents
 1    Types of systems   
 2    Software tools   
 3    Benefits   
 4    Generic data warehouse environment   
 5    History   
 6    Information storage   
o 6.1     Facts   
o 6.2     Dimensional vs. normalized approach for storage of data   
 7    Top-down versus bottom-up design methodologies   
o 7.1     Bottom-up design   
o 7.2     Top-down design   
o 7.3     Hybrid design   
 8    Data warehouses versus operational systems   
 9    Evolution in organization use   
 10     See also   
 11     References   
 12     Further reading   
 13     External links   

Types of systems[edit]
Data mart
A data mart is a simple form of a data warehouse that is focused on a single subject 
(or functional area), such as sales, finance or marketing. Data marts are often built and 
controlled by a single department within an organization. Given their single-subject 
focus, data marts usually draw data from only a few sources. The sources could be 
internal operational systems, a central data warehouse, or external data.
[1]
Online analytical processing(OLAP)
Is characterized by a relatively low volume of transactions. Queries are often very 
complex and involve aggregations. For OLAP systems, response time is an 
effectiveness measure. OLAP applications are widely used by Data Mining techniques. 
OLAP databases store aggregated, historical data in multi-dimensional schemas 
(usually star schemas). OLAP systems typically have data latency of a few hours, as 
opposed to data marts, where latency is expected to be closer to one day.
Online Transaction Processing(OLTP)
Is characterized by a large number of short on-line transactions (INSERT, UPDATE, 
DELETE). OLTP systems emphasize very fast query processing and maintainingdata 
integrityin multi-access environments. For OLTP systems, effectiveness is measured 
by the number of transactions per second. OLTP databases contain detailed and 
current data. The schema used to store transactional databases is the entity model 
(usually3NF).[2]
Predictive analysis
Predictive analysis is aboutfindingand quantifying hidden patterns in the data using 
complex mathematical models that can be used topredictfuture outcomes. Predictive 
analysis is different from OLAP in that OLAP focuses on historical data analysis and is 
reactive in nature, while predictive analysis focuses on the future. These systems are 
also used for CRM (Customer Relationship Management).[3]
Software tools[edit]
The typical extract-transform-load (ETL)-based data warehouse usesstaging,data 
integration, and access layers to house its key functions. The staging layer or staging 
database stores raw data extracted from each of the disparate source data systems. 
The integration layer integrates the disparate data sets by transforming the data from 
the staging layer often storing this transformed data in anoperational data store(ODS) 
database. The integrated data are then moved to yet another database, often called 
the data warehouse database, where the data is arranged into hierarchical groups 
often called dimensions and into facts and aggregate facts. The combination of facts 
and dimensions is sometimes called astar schema. The access layer helps users 
retrieve data.[4]
This definition of the data warehouse focuses on data storage. The main source of 
the data is cleaned, transformed, cataloged and made available for use by managers 

and other business professionals fordata mining,online analytical processing,market 
researchanddecision support.[5]However, the means to retrieve and analyze data, 
toextract, transform and loaddata, and to manage thedata dictionaryare also 
considered essential components of a data warehousing system. Many references to 
data warehousing use this broader context. Thus, an expanded definition for data 
warehousing includesbusiness intelligence tools, tools toextract, transform and 
loaddata into the repository, and tools to manage and retrievemetadata.
Benets[edit]
A data warehouse maintains a copy of information from the source transaction 
systems. This architectural complexity provides the opportunity to:
 Congregate data from multiple sources into a single database so a single query 
engine can be used to present data.
 Mitigate the problem of database isolation level lock contention in transaction 
processing systems caused by attempts to run large, long running, analysis queries 
in transaction processing databases.
 Maintaindata history, even if the source transaction systems do not.
 Integrate data from multiple source systems, enabling a central view across the 
enterprise. This benefit is always valuable, but particularly so when the organization 
has grown by merger.
 Improvedata quality, by providing consistent codes and descriptions, flagging or
even fixing bad data.
 Present the organization's information consistently.
 Provide a single common data model for all data of interest regardless of the 
data's source.
 Restructure the data so that it makes sense to the business users.
 Restructure the data so that it delivers excellent query performance, even for 
complex analytic queries, without impacting theoperational systems.
 Add value to operational business applications, notablycustomer relationship 
management(CRM) systems.
 Make decision–support queries easier to write.
Generic data warehouse environment[edit]
The environment for data warehouses and marts includes the following:
 Source systems that provide data to the warehouse or mart;
 Data integration technology and processes that are needed to prepare the data 
for use;
 Different architectures for storing data in an organization's data warehouse or 
data marts;