Data mart

<h2 id="reasons-for-creating-a-data-mart">Reasons for creating a data mart</h2>
<ul><li>Easy access to frequently needed data</li>
<li>Creates a collective view by a group of users</li>
<li>Improves end-user <a href="/facts/Response_time_(technology)/3kQrmztQ">response time</a></li>
<li>Ease of creation</li>
<li>Lower cost than implementing a full data warehouse</li>
<li>Potential users are more clearly defined than in a full data warehouse</li>
<li>Contains only business essential data and is less cluttered.</li>
<li>It has key  data information</li></ul>
<h2 id="design-schemas">Design schemas</h2>
<ul><li><a href="/facts/Star_schema/hXsGMSKf">Star schema</a> - fairly popular design choice; enables a <a href="/facts/Relational_database/pEKGmJLy">relational database</a> to emulate the analytical functionality of a <a href="/facts/Multidimensional_database/VNNbIlD0">multidimensional database</a></li>
<li><a href="/facts/Snowflake_schema/wk72Cr89">Snowflake schema</a></li>
<li>Activity schema - a <a href="/facts/Time_series/fSXPR817">time-series</a> based schema</li></ul>
<h2 id="data-mart-vs-data-warehouse">Data mart vs data warehouse</h2>
<p>Data warehouse:
</p>
<ul><li>Holds multiple subject areas</li>
<li>Holds very detailed information</li>
<li>Works to integrate all data sources</li>
<li>Does not necessarily use a <a href="/facts/Dimensional_modeling/fNywevVS">dimensional model</a> but feeds dimensional models.</li></ul>
<p>Data mart:
</p>
<ul><li>Often holds only one subject area- for example, Finance, or Sales</li>
<li>May hold more summarized data (although it may hold full detail)</li>
<li>Concentrates on <a href="/facts/Information_integration/veGbc8F7">integrating information</a> from a given subject area or set of source systems</li>
<li>Is built focused on a dimensional model using a star schema.</li></ul>
<h2 id="dependent-data-mart">Dependent data mart</h2>
<p>According to the <a href="/facts/Bill_Inmon/haumq5vG">Inmon</a> school of data warehousing, a dependent data mart is a logical subset (<a href="/facts/View_(database)/6B00yj6A">view</a>) or a physical subset (extract) of a larger <a href="/facts/Data_warehouse/we5rblq9">data warehouse</a>, isolated for one of the following reasons:
</p>
<ul><li>A need refreshment for a special <a href="/facts/Data_model/j1YyzpJ3">data model</a> or <a href="/facts/Logical_schema/K0EbrogL">schema</a>: e.g., to restructure for <a href="/facts/Online_analytical_processing/VNNbIlD0">OLAP</a>.</li>
<li>Performance: to offload the data mart to a separate <a href="/facts/Computer/SQe1aK05">computer</a> for greater efficiency or to eliminate the need to manage that workload on the centralized data warehouse.</li>
<li>Security: to separate an authorized data subset selectively.</li>
<li>Expediency: to bypass the data governance and authorizations required to incorporate a new application on the Enterprise Data Warehouse.</li>
<li>Proving Ground: to demonstrate the viability and ROI (return on investment) potential of an application prior to migrating it to the Enterprise Data Warehouse.</li>
<li>Politics: a coping strategy for IT (Information Technology) in situations where a user group has more influence than funding or is not a good citizen on the centralized data warehouse.</li>
<li>Politics: a coping strategy for consumers of data in situations where a data warehouse team is unable to create a usable data warehouse.</li></ul>
<p>According to the Inmon school of data warehousing, tradeoffs inherent with data marts include limited <a href="/facts/Scalability/jh7JomPa">scalability</a>, <a href="/facts/Data_duplication/sHuJ8zya">duplication of data</a>, <a href="/facts/Data_inconsistency/GmTWXGKA">data inconsistency</a> with other silos of information, and inability to leverage enterprise sources of data.
</p><p>The alternative school of data warehousing is that of <a href="/facts/Ralph_Kimball/obMfhEK5">Ralph Kimball</a>. In his view, a data warehouse is nothing more than the union of all the data marts. This view helps to reduce costs and provides fast development, but can create an inconsistent data warehouse, especially in large organizations. Therefore, Kimball's approach is more suitable for small-to-medium corporations.<a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a>
</p>
<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Data_warehouse/we5rblq9">Data warehouse</a></li>
<li><a href="/facts/Enterprise_architecture/7UNSuE0l">Enterprise architecture</a></li>
<li><a href="/facts/OLAP_cube/dLTSCod6">OLAP cube</a></li></ul>

<h2 id="external-links">External links</h2>

<h2 id="references">References</h2>

<ol>
<li id="fn:1"><p>"What Is a Data Mart?". IBM. 2021-10-21. Retrieved 2024-12-16. <a href="https://www.ibm.com/topics/data-mart" target="_blank">https://www.ibm.com/topics/data-mart</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li>
<li id="fn:2"><p>Inmon, William (July 18, 2000). "Data Mart Does Not Equal Data Warehouse". DMReview.com. Archived from the original on April 20, 2011. <a href="https://web.archive.org/web/20110420134556/http://csis.bits-pilani.ac.in/faculty/goel/Data%20Warehousing/Articles/Data%20Marts/dataWarehouse_com%20%20Article_DM%20VS%20DW.htm" target="_blank">https://web.archive.org/web/20110420134556/http://csis.bits-pilani.ac.in/faculty/goel/Data%20Warehousing/Articles/Data%20Marts/dataWarehouse_com%20%20Article_DM%20VS%20DW.htm</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li>
<li id="fn:3"><p>Silvers, Fon (2008). Building and Maintaining a Data Warehouse. Boca Raton, Florida: CRC Press. p. 128. ISBN 978-1-4200-6462-9. <a href="978-1-4200-6462-9" target="_blank">978-1-4200-6462-9</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li>
<li id="fn:4"><p>Caudill, Herb (April 1, 2018). "Excel Hell: A cautionary tale". Medium. Retrieved October 19, 2021. <a href="https://medium.com/all-the-things/a-single-infinitely-customizable-app-for-everything-else-9abed7c5b5e7" target="_blank">https://medium.com/all-the-things/a-single-infinitely-customizable-app-for-everything-else-9abed7c5b5e7</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li>
<li id="fn:5"><p>Ponniah, Paulraj (2010). Data Warehousing Fundamentals for IT Professionals. Hoboken, New Jersey: Wiley. pp. 29–32. ISBN 978-0470462072. <a href="978-0470462072" target="_blank">978-0470462072</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li>
</ol>

Data mart open-in-new

Data mart