Research A Scholarly Paper On Databases, Warehouses, And Adv
Research A Scholarly Paper On Databases Warehouses And Advanced Data
Research a scholarly paper 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 exceed one full page in length, address 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. You must provide at least one APA reference for your resource and corresponding in-text citations. You must provide the referenced resource URL/DOI in the APA reference. Do not use the Textbook as a referenced resource.
Paper For Above instruction
DM Types
The selection of a specific Data Management System (DMS) type is primarily determined by the organizational requirements, the complexity of the data, and the intended use cases. Different types of DMSs, such as relational databases, data warehouses, or advanced data lakes, are suited to particular operational or analytical needs. Relational databases are ideal for transactional systems requiring real-time data processing and consistency, while data warehouses are optimized for analytical processing and decision-making, aggregating large volumes of historical data. Advanced data systems, like data lakes, accommodate unstructured or semi-structured data, supporting big data analytics and machine learning applications. The organization's strategic goals, data volume, and speed of data access heavily influence the choice of system. For instance, a corporation focused on real-time transaction processing might prioritize relational databases; conversely, a research institution aiming to perform extensive data analysis could lean towards data warehouses or lakes. Cost considerations, scalability, and compatibility with existing infrastructure also play crucial roles. These factors collectively determine which DMS type best aligns with the organization’s data needs, operational demands, and future growth plans.
The decision process involves assessing data velocity, variety, and volume—often referred to as the three Vs of big data. If data is rapidly ingested and needs immediate analysis, a system capable of handling high throughput like in-memory databases or NoSQL stores might be chosen. If the requirement centers around historical data analysis for strategic insights, data warehouses designed with star or snowflake schemas are preferred for their optimized query performance. Furthermore, technological advancements continuously influence these choices; for example, innovations in cloud-based DMSs offer scalable and flexible solutions that can dynamically adapt to changing requirements. Ultimately, the deciding factors boil down to data types, organizational processes, performance needs, and cost-effectiveness, all of which determine the most suitable DMS type for a given context.
Reflecting on this topic, I was intrigued by how critical the match between business needs and DMS types is for organizational success. Choosing the wrong system can lead to increased costs, inefficiencies, and poor data utilization, emphasizing the importance of strategic planning in data architecture. The evolution from traditional relational databases to more flexible, scalable architectures like data lakes demonstrates how technological innovation continues to shape DMS selection. This understanding underscores the importance of comprehensive assessment and alignment between organizational goals and data infrastructure, which is vital for effective decision-making and competitive advantage.
References
Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: issues, challenges, tools and good practices. Advances in Computers, 91, 64-165. https://doi.org/10.1016/C2012-0-07189-7