Technology Paper on Data Warehouses and Data Marts

Data Warehouses and Data Marts

Data mart is an information store that deals with a specific area in an organization. As such, it stores data related to that particular section. A sales data mart will store information related to sales only. For example, it may have sales ID, product ID, date, price, and code among others. On the other hand, data warehouses store information related to several departments within an organization in a single location (DataOnFocus, 2015). This means that they combine information from several data marts. For example, a data warehouse may combine information related to finance, sales, human resource, and information technology departments into a single location.

Factors such as scope, size, cost, integration, creation, and performance differentiate these data structures (DataOnFocus, 2015). Firstly, data warehouses are much bigger since the hold a lot of data as compared to data marts that only hold specific departmental information. Secondly, data warehouses integrate multiple and different information pieces whereas data marts require less integration since data comes from a single department (Standen, 2008). In this regard, it is easy to integrate information from the finance department only and difficult to integrate data from finance, sales, and human resource combined. Thirdly, creation of data warehouses is time consuming and difficult based on the size and nature of data. Conversely, creation of data marts requires less time and analysis (DataOnFocus, 2015). Additionally, the costs of building data warehouses are much high than those of data marts. This is because data warehouses need large servers, memory, and disk space. In relation to performance, data marts tend to perform better due to the simple nature of data stored.

Organizations can use these structures to acquire information by getting data to people and building architecture (Standen, 2008). Getting data to people relates to providing better tools to ensure that analysis tools such as spreadsheets are properly functional.  On the other hand, building architecture concerns buying infrastructure and project management tools necessary for collecting data. For example, having a server with many data marts would be cheaper than having multiple servers.

 

References

DataOnFocus. (2015). Data Mart vs Data Warehouse. Retrieved from http://www.dataonfocus.com/data-mart-vs-data-warehouse/.

Standen, J. (2008). Data Warehouse vs Data Mart. Retrieved from http://www.datamartist.com/data-warehouse-vs-data-mart.