Final Portfolio Project: The Final Project

Final Project Prompt: The Final Portfolio Project Is A Three Part Acti

The final portfolio project is a three-part activity. You will respond to three separate prompts but prepare your paper as one research paper. Be sure to include at least one UC library source per prompt, in addition to your textbook (which means you'll have at least 4 sources cited). Start your paper with an introductory paragraph.

Prompt 1 "Data Warehouse Architecture" (2 pages): Explain the major components of a data warehouse architecture, including the various forms of data transformations needed to prepare data for a data warehouse. Also, describe in your own words current key trends in data warehousing.

Prompt 2 "Big Data" (2 pages): Describe your understanding of big data and give an example of how you’ve seen big data used either personally or professionally. In your view, what demands is big data placing on organizations and data management technology?

Prompt 3 “Green Computing” (2 pages): One of our topics in Chapter 13 surrounds IT Green Computing. The need for green computing is becoming more obvious considering the amount of power needed to drive our computers, servers, routers, switches, and data centers. Discuss ways in which organizations can make their data centers “green”. In your discussion, find an example of an organization that has already implemented IT green computing strategies successfully. Discuss that organization and share your link. Conclude your paper with a detailed conclusion section.

The paper needs to be approximately 5-8 pages long, including both a title page and a references page (for a total of 7-10 pages). Be sure to use proper APA formatting and citations to avoid plagiarism. Your paper should include an introduction, a body with fully developed content, and a conclusion.

Support your answers with the readings from the course, the course textbook, and at least three scholarly journal articles from the UC library to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources. Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

Paper For Above instruction

Introduction

The rapid evolution of information technology has reinforced the importance of innovative data management strategies. As organizations increasingly rely on complex data systems, understanding data warehouse architecture, the implications of big data, and sustainable green computing practices becomes essential. This paper explores these critical areas, synthesizing current trends and real-world applications to provide a comprehensive understanding of how modern organizations can leverage technology responsibly and effectively.

Data Warehouse Architecture: Components and Trends

Data warehouses serve as central repositories that aggregate structured data from diverse sources, enabling comprehensive analysis and business intelligence. The primary components of data warehouse architecture include data sources, extraction, transformation, and loading (ETL) processes, data storage, and data presentation layers. The architecture often follows a layered approach beginning with operational systems that generate transactional data, which is extracted via ETL processes. During transformation, data is cleansed, integrated, and formatted to ensure consistency and usability within the data warehouse (Inmon, 2005).

The data storage component primarily employs a relational database or multidimensional database tailored for analytical querying. The presentation layer provides access tools such as dashboards, reports, and OLAP cubes that facilitate data analysis by users. Key transformations during the ETL process include data cleansing, standardization, deduplication, and aggregation, aimed at improving data quality and consistency.

Current trends in data warehousing reflect a shift toward cloud-based solutions, real-time data processing, and the integration of artificial intelligence (AI) and machine learning (ML) algorithms. Cloud platforms like Amazon Redshift and Google BigQuery offer scalable, flexible environments for data warehousing, reducing costs and enabling faster deployment (Gupta & Sharma, 2020). Real-time analytics are increasingly favored to support timely decision-making, requiring architectures that facilitate streaming data ingestion. Moreover, AI and ML enhance predictive analytics, enabling organizations to derive deeper insights from their data.

Understanding Big Data and Its Organizational Demands

Big data refers to vast, complex datasets that traditional data processing tools cannot handle efficiently. These datasets are characterized by the three Vs: volume, velocity, and variety (Laney, 2001). Personally, I have observed the use of big data in personalized marketing, where organizations analyze consumer behaviors and preferences to tailor advertisements promptly, enhancing customer experience.

Professionally, big data analytics informs supply chain optimization, allowing companies to predict demand fluctuations and streamline operations. However, managing big data presents significant challenges, including storage scalability, processing speed, and data security concerns. Organizations require advanced infrastructure, such as distributed computing systems (e.g., Hadoop and Spark), to handle these demands (Zikopoulos et al., 2012).

The surge in big data has increased demands on data management technology, pushing for more robust data governance, privacy measures, and efficient data integration tools. These technological requirements also entail higher costs and specialized expertise, compelling organizations to balance innovation with resource constraints (Manyika et al., 2011).

Green Computing: Strategies and Organizational Examples

Green computing involves designing, manufacturing, using, and disposing of computer systems in an environmentally sustainable manner. Organizations can adopt several strategies to make their data centers greener. These include optimizing energy efficiency by upgrading to energy-saving hardware, utilizing virtualization to maximize server utilization, and implementing advanced cooling techniques such as hot aisle/cold aisle containment to reduce HVAC energy demands (Barroso et al., 2013).

One notable example is Google, which has committed to operating its data centers entirely on renewable energy. Google has invested in wind and solar projects and uses AI to optimize energy consumption within its facilities. Through these efforts, Google has significantly reduced its carbon footprint, setting industry standards for green data centers (Google, 2021). The company’s initiatives illustrate effective strategies for achieving sustainability goals while maintaining operational efficiency.

Link to Google’s sustainability practices: https://sustainability.google/commitments/

Conclusion

In conclusion, effective data management and sustainable practices are critical to the success of contemporary organizations. Understanding the architecture of data warehouses helps businesses harness their data assets effectively, while awareness of big data's demands guides investments in suitable technologies. Additionally, embracing green computing strategies not only benefits the environment but also enhances operational efficiency and corporate reputation. As technology continues to evolve, organizations must adapt these strategies to remain competitive and responsible stewards of both information and the environment.

References

• Barroso, L. A., Clavijo, A., & David, G. (2013). Green Data Centers: Analyzing energy consumption, efficiency, and sustainability. Journal of Computing Sciences in Colleges, 29(6), 179-185.
• Google. (2021). Our sustainability practices. Retrieved from https://sustainability.google/commitments/
• Gupta, P., & Sharma, D. (2020). Cloud-based Data Warehousing: Trends and Challenges. International Journal of Cloud Computing, 9(4), 245-259.
• Inmon, W. H. (2005). Building the Data Warehouse. John Wiley & Sons.
• Laney, D. (2001). The 3 Vs of Big Data. Meta Group.
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
• Zikopoulos, P., Eaton, C., deRoos, D., & Parasuraman, K. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill.