Data Warehouse Architecture. 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. You can find examples in the UC Library.

An information system called a data warehouse contains historical and exchangeable data from one or more sources (Yang et al., 2019). The data warehouse concept simplifies the organizational reporting and analysis process. As an information system containing historical and exchanged data from various sources, data warehouse architecture is complex. There are three approaches to creating data warehouse tiers: single-tier, two-tier, and three-tier (Smith, 2022). To make the entire ecosystem functional, manageable, and accessible, the data warehouse is built on an RDBMS server as the main information repository, surrounded by several fundamental data warehouse components. As the main database and framework of the data warehouse environment, the data warehouse database is one of the key elements of the data warehouse design. The database uses RDBMS technology in its implementation. However, this type of implementation is limited by the fact that standard RDBMS systems are designed to handle transactional databases rather than store data. The second part is tools for procurement, acquisition, cleansing and transformation (ETL) (Yang et al., 2019). The transformations, aggregations, and other customizations required to transform the data into a single format in the data warehouse are performed using data source, transformation, and migration tools. ETL (Extract, Transform and Load) tool is another name for it. continue…

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