w3reference home
Databases Tutorial


Bookmark and Share

Data Mart

What is the difference between a data warehouse and a data mart?
In general a Data Warehouse is used on an enterprise level, while Data Marts is used on a business division/department level. A data mart only contains the required subject specific data for local analysis.

A database, or collection of databases, designed to help managers make strategic decisions about their business. Whereas a data warehouse combines databases across an entire enterprise, data marts are usually smaller and focus on a particular subject or department. Some data marts, called dependent data marts, are subsets of larger data warehouses. A data mart is a simpler form of a data warehouse focused on a single subject (or functional area) such as sales, finance, marketing, HR etc. Data Mart represents data from single business process.

A data mart can run in size from megabytes to gigabytes

Data warehouse may run from gigabytes to Terabytes

Three technologies are needed to generate on-demand data marts:
  • An accessible meta data repository that users can query.
  • An extract tool that can get to the data on demand.
  • A target database management system (DBMS) that can automatically generate a database.

    The extract tool should provide access to data in a variety of data sources. Data is typically stored in several places: a data warehouse, operational data stores, operational databases and flat files. Some data stores can be extracted in near-real time, such as a data warehouse. Others have to wait so that key business functions are not disturbed. The extraction should be scheduled automatically, with clear communication to the user of the time schedule. The source should be relatively transparent to the user, while ensuring that the meta data descriptions provide enough context for the user to know that the correct source is being tapped.

    To provide easier data analysis, this database should be relational. However, substantial technical expertise is required to design and create a relational database that has acceptable performance characteristics. The major technology advance in this area has been the introduction of databases that permit the effective creation of in-memory databases. In turn, this technology delivers analysis performance without the need for physical database design in the traditional sense. This provides a great solution for the creation of short-lived databases that are needed quickly, but only for limited periods of time.

    Each Data Mart may contain several Fact Tables each with many Dimension Tables
  • Code Validator
    Learn FTP
    Color finder
    Link Checker
    Free web designs
    Coming soon!
    Interview Questions...
    'w3reference : Learn by examples ... Advanced to beginner's tutorials ...'
    Ajax: AJAX tutorial1 | Apache: Apache HTTP Server | Restarting Apache | CSS: CSS Border | CSS Syntax | CSS Selector | CSS Comment | CVS: CVS Release | CVS Login | CVS Logout | CVS Annotate | Databases: Rolap Tutorial | OLAP Tutorial | OLTP Tutorial | data warehousing | Expect: HTML: html | Linux: Dot (.) conf files | Linux Mount Point | Linux Filesystem | SSH Tutorial | Linux Commands: cal | cat | cfdisk | chroot | MySQL: MySQL Commands | PHP: PHP Basics | PHP Variables | PHP Output (echo/print) | PHP String Concat | PL/SQL: PL/SQL Data Types | PL/SQL Control Structures | PL/SQL File Extensions | PL/SQL DBMS_OUTPUT package | Python: My first Python program | Shell: Starting Bash | Bash Redirection | Bash Pipes | Bash Variables | SQL: SQL Transactions | SQL Constraints | SQL Drop | SQL Union & Union All | SVN: svn architecture | SVN Repository | SVN Import | SVN Checkout | Tech: soap | Web Designing: Web Hosting | HTML/XHTML/CSS code validator | Learn FTP | Search Engine Optimization Tips | www: XML: XML vs HTML | XML Syntax | XML Tags, Elements and Attributes | XML Namespaces |
    Sitemap | Disclaim | Privacy Policy | Contact | ©2007-2009 w3reference.com All Rights Reserved.