Assignment 1: This Is A Required Assignment Worth 45 Points

Assignment 1this Is A Required Assignment Worth 45 Points And Must

Research a scholarly paper or professional video on "Databases, Warehouses and Advanced Data Management Systems" and reflect on only one (1) of the following topics: "DM Types": What determines which type of Data Management System is being used? "Importance": How important is the Data Management system in conducting SAD? "SA": What is the role of the Systems Analyst to propose new Data Management solutions? NOTE: You must copy and paste the topic ("DM Types" or "Importance" or "SA") at the start of your paper to provide a context for your answer.

This paper must be between words on what caught your eye and reflect on what you read. Do not add extraneous text that does not address the question - do not add an introduction or conclusion. Do not copy and paste text from the referenced resource.

Paper For Above instruction

The selected topic for this reflection is "DM Types," which explores the factors influencing the choice of specific data management systems. The decision about which type of Data Management System (DMS) to employ hinges on multiple considerations, primarily the nature of the data, organizational needs, scalability requirements, and performance expectations.

One of the critical determinants is the type of data involved. For example, transactional data necessitates robust, reliable systems such as relational databases that can support multiple concurrent users with real-time processing capabilities (Connolly & Begg, 2015). Conversely, analytical or historical data often require data warehouses which optimize read-heavy operations, enabling complex queries across large datasets (Kimball & Ross, 2013). Therefore, the inherent characteristics of data—whether structured or unstructured—play a pivotal role in selecting an appropriate system.

Organizational requirements also influence the choice. Businesses with intensive transaction needs, such as banks, typically prioritize systems that ensure data integrity, speed, and security—attributes common to relational database management systems (RDBMS) like Oracle or SQL Server (Codd, 1970). Meanwhile, companies focused on business intelligence and analytics may lean toward data warehouses and data lakes, which facilitate data integration from multiple sources, enabling comprehensive analysis (Inmon, 2005). Additionally, the scale of data and provisioning for future growth impact decision-making, with cloud-based and distributed systems offering scalability and flexibility (Vohra, 2017).

Performance and operational needs also influence the choice. For instance, real-time decision-making in high-frequency trading platforms demands highly optimized systems capable of handling massive data throughput with minimal latency (Madden et al., 2014). On the other hand, data archival and batch processing applications are well-served by systems designed for high-volume, low-latency batch operations, such as Hadoop-based platforms (Shvachko et al., 2010). The technological environment and existing infrastructure further modulate the decision process, considering compatibility, cost, and ease of maintenance.

In summary, the determination of which Data Management System type to use is contingent upon data characteristics, organizational needs, performance demands, and scalability. These factors collectively guide decision-makers in selecting systems that optimize data utility and operational efficiency, ensuring aligned support for organizational objectives and technological infrastructure.

References

  • Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377-387.
  • Connolly, T., & Begg, C. (2015). Database Systems: A Practical Approach to Design, Implementation, and Management (6th ed.). Pearson.
  • Inmon, W. H. (2005). Building the Data Warehouse (4th ed.). Wiley.
  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. John Wiley & Sons.
  • Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2014). The Cost of Accuracy in Sensor Data. Proceedings of the VLDB Endowment, 7(12), 1216-1219.
  • Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The Hadoop Distributed File System. Proceedings of the IEEE 26th Symposium on Mass Storage Systems and Technologies.

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