Data Warehouses, Data Marts, and DBMS Functions Explained
This paper examines core concepts in enterprise data management, beginning with a promotional overview of a fictional data repository service that highlights real-world offerings such as cloud hosting, big data consultancy, and analytics advisory services. It then defines the functions of data warehouses, including legacy system integration and query segmentation, before describing the features of data marts as specialized subsets aligned to business unit needs. The paper differentiates supervised from unsupervised data mining, explaining how hypothesis development distinguishes the two approaches. Finally, it provides an overview of Database Management Systems (DBMS), covering relational and object-oriented database types and profiling leading vendors such as Oracle, IBM, Microsoft, SAP/Sybase, and Teradata.
- Data Repository Services Overview: Brochure overview of data repository service offerings
- Functions of a Data Warehouse: Integration, ontology support, and query segmentation functions
- Features of a Data Mart: Data mart design, scope, and business unit alignment
- Supervised vs. Unsupervised Data Mining: Hypothesis-driven versus exploratory data mining methods
- Database Management Systems and Their Functions: DBMS definition, relational and object-oriented database types
- Popular DBMS Platforms and Market Leaders: Oracle, IBM, Teradata, and big data market dominance
✍️ How to write this paper — guide, tools & examples ▾
What makes this paper effective
- Clearly distinguishes related but distinct concepts — data warehouses, data marts, and DBMS — giving each its own focused treatment rather than conflating them.
- Grounds abstract technical concepts in practical business context, such as connecting data mart design to functional areas like accounting, finance, and marketing.
- Supports claims with citations throughout, demonstrating engagement with the academic literature on database management and data mining.
Key academic technique demonstrated
The paper uses definition-then-application structuring: each concept is first defined in technical terms, then illustrated with real-world examples or vendor comparisons. This technique helps readers at varying knowledge levels follow the argument and understand why each concept matters in practice, not just in theory.
Structure breakdown
The paper opens with a brochure-style service overview that frames the practical context. It then moves through four substantive sections in logical order — warehouse functions, data mart features, mining methodology, and DBMS overview — before closing with a market landscape survey of dominant database vendors. References are formatted in APA style and span peer-reviewed journals in information systems, database management, and manufacturing.
Data Repository Services Overview
Powered By Excellence — Data Repository Service
Your Data Is Critical to Your Growth!
Powered By Excellence is the only data repository service with globally located data centers across every continent, each equipped with specific security, reliability, and fault redundancy systems. Our staff includes world-class experts on the following platforms: IBM, Microsoft, Oracle, MySQL, Informix, Sybase, Teradata, and SAS — all available in-house as part of our consulting services division.
Services Offered: Analytics Advisory Services; Big Data Consultancy (MapR and Hadoop expertise for gaining insights from very large datasets); Custom Software Development; Database Hosting; SaaS Application Support; Scalable File Storage; Private Cloud Hosting (dedicated storage and unlimited virtual machines).
Customer Benefits: High performance with a world-class platform; 24/7 Administrator Access; Unlimited Virtual Machine Use; Service Level Agreement (SLA) metrics available 24/7.
Three reasons why your business needs to collect and analyze data:
Gain valuable customer intelligence and insight to design better sales and service campaigns, driving up Lifetime Customer Value.
Gain insights into managing suppliers and entire supply chains more effectively.
Know which distributors and dealers are the highest and lowest performing, and why.
Understand how gross margins and profitability change across an entire product lifecycle — and why.
Trusted Provider of Data Repository Services — ISO 16363 and DIN 31644 Certified (2013).
Functions of a Data Warehouse
The functions of a data warehouse include integrating the diverse and often disparate legacy database and transactional systems of an enterprise into a single, unified system of record (Benander, Benander, Fadlalla, & Gregory, 2000). Data warehouses also require an intensive amount of integration platform support. Of the many integration technologies now being adopted to unify data warehouses, XML is increasingly being adopted as a standard (Van, 2002). Data warehouses are often designed to support multiple ontologies of data structures essential for running a business, which enforces a logical file and data structure throughout the entire organization (Selma, Ilyas, Ladjel, Eric, Stéphane, & Michael, 2012).
A second function of a data warehouse is to segment out the data used more frequently for queries from the data in the actual warehouse used for reporting and analysis. This second aspect of a data warehouse ensures higher levels of performance on more frequent queries, preventing routine query activity from degrading the performance of deeper analytical workloads.
Features of a Data Mart
A data mart is often a smaller subset of a data warehouse or other data platform, typically designed to support the needs of a given business strategy or objective (Benander, Benander, Fadlalla, & Gregory, 2000). The term data mart can apply equally to evaluational data, operational data, metadata, or spatial data. Data marts are designed to align with the strategic information requirements of a given business unit or functional area of an organization (Benander, Benander, Fadlalla, & Gregory, 2000). Examples include dedicated data marts for accounting, finance, marketing, sales, and service.
A data mart is often designed as a one-dimensional model or star schema, composed as a fact table or multi-dimensional table (Benander, Benander, Fadlalla, & Gregory, 2000). This structure allows business users to query their specific domain of data efficiently without accessing the full warehouse, improving both performance and data governance.
References
Benander, A., Benander, B., Fadlalla, A., & Gregory, J. (2000). Data warehouse administration and management. Information Systems Management, 17(1), 71–80.
Choudhary, A. K., Harding, J. A., & Tiwari, M. K. (2009). Data mining in manufacturing: A review based on the kind of knowledge. Journal of Intelligent Manufacturing, 20(5), 501–521.
He, Z., Lee, B. S., & Snapp, R. (2005). Self-tuning cost modeling of user-defined functions in an object-relational DBMS. ACM Transactions on Database Systems, 30(3), 812–812.
Inmon, W. H. (1996). The data warehouse and data mining. Communications of the ACM, 39(11), 49–50.
Liu, B., Cao, S. G., & He, W. (2011). Distributed data mining for e-business. Information Technology and Management, 12(2), 67–79.
Selma, K., Ilyas, B., Ladjel, B., Eric, S., Stéphane, J., & Michael, B. (2012). Ontology-based structured web data warehouses for sustainable interoperability: Requirement modeling, design methodology and tool. Computers in Industry, 63(8), 799.
Van, d. H. (2002). Database management system and XML: Interchange of data. Information Systems Management, 19(1), 94–96.
Zhang, Y., & Sheth, D. (2006). Mining software repositories for model-driven development. IEEE Software, 23(1), 82–90.
Always verify citation format against your institution’s current style guide requirements.