The final portfolio project is a three- part activity. You will respond to three separate prompts but prepare your paper as one research paper. (which means you’ll have at least 4 sources cited). Start your paper with an introductory paragraph. Prompt 1 “Data Warehouse Architecture” (2-3 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” (1-2 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” (1-2 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.

Data Warehouse Architecture

A data warehouse is a centralized repository that stores large amounts of data collected from various sources within an organization. It is designed to support business intelligence and analytics activities by providing a consolidated view of data that is structured for analysis. In order to effectively design and implement a data warehouse, it is important to understand its major components and the various forms of data transformations needed.

The major components of a data warehouse architecture include data sources, data integration, data storage, and data access. Data sources refer to the systems that generate the data, such as transactional databases, operational systems, external data feeds, and even spreadsheets. These data sources may contain data in different formats and structures, and therefore, data integration is a critical component. Data integration involves processes such as data extraction, data cleansing, data transformation, and data loading. These processes ensure that data is consistent, accurate, and suitable for analysis in the data warehouse.

Data storage in a data warehouse is typically in the form of a schema, also known as a dimensional model, which organizes data into dimensions and facts. Dimensions represent the different attributes or characteristics of the data, such as time, location, and product. Facts, on the other hand, are the numerical measurements or metrics that are being analyzed, such as sales revenue or customer count. The schema design in a data warehouse is optimized for query performance and supports complex analysis and reporting.

Data access in a data warehouse is facilitated through the use of tools and technologies that allow users to query and retrieve data. These tools enable users to perform data analysis, generate reports, and create dashboards. Some examples of data access tools include SQL-based query languages, online analytical processing (OLAP) tools, and data visualization tools.

In terms of current key trends in data warehousing, there are several notable developments. First, there is a growing emphasis on real-time and near-real-time data warehousing, where data is updated and made available for analysis in real-time or near-real-time. This trend is driven by the increasing need for organizations to make data-driven decisions quickly.

Another important trend is the adoption of cloud-based data warehousing solutions. Cloud-based data warehouses provide scalability, flexibility, and cost-effectiveness, as they eliminate the need for organizations to build and maintain their own physical infrastructure. Additionally, cloud-based data warehouses can easily integrate with other cloud-based services and data sources.

Furthermore, there is a growing recognition of the importance of data governance and data quality in data warehousing. Organizations are investing in data governance initiatives to ensure that data in the data warehouse is accurate, reliable, and compliant with regulatory requirements.

In summary, a data warehouse architecture consists of major components such as data sources, data integration, data storage, and data access. The various forms of data transformations, such as extraction, cleansing, and transformation, are needed to prepare data for a data warehouse. Current key trends in data warehousing include real-time and near-real-time data warehousing, adoption of cloud-based solutions, and focus on data governance and data quality.

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