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

Warehousing provides cloud data storage, which allows some companies to store a backup of the information that that had. Warehouse data is used in businesses where it is used in the process of In-memory analytical engines, where it aids in the process of performing tasks and operations when executing particular tasks. The in-memory analytical engines include tasks like importing large amounts of data and processing them at fast speeds. A data lake is a term that is utilized in the warehouses in the processes of defining the data schemes when it comes to the reading process. The data lake is used in the process of processing data while they use efficient tools such as the Hadoop system, and Data lake is used in the process of utilizing the distributed storage. The data lake allows a chance of post and pre-processing of information with reduced time and cost, thus providing efficiency. (Tari, 2018) Lastly, warehouses consist of waremarts which are used in the process of production of goods. The primary purpose of utilizing data lines is that they have a distinct advantage of providing a quick solution through the containment of the data that was sent in the warehouse. In case ware marts find data is not good enough, they isolate the data until where the administrator looks at the data that was isolated.

Prompt 2: my understanding of big data and an example where I have seen personally the big data being utilized in various organizations and data management technology. Mainly big data is utilized in the organizations. Big data analytics is mainly composed of many generalized complex pro- cesses that are used in examining varied and vast data to uncover information. An example of companies that use robust management strategies is Google and Facebook.

6 7 1 8 9 10 8 11 The information technology experts of these companies mainly participate in the process of evaluating extensive data which will be utilized in their organizations. Big data analytics make it possible for these organizations make it possible to execute their processes faster thus enhancing the ex- perience of the user.( Wang, 2018) Big data analytics helps organizations in harnessing data that it uses in the identification of new opportunities in the market. This makes big data analytics in this organization to lead inefficient business operations, maximizing their profits, and making their cus- tomers happier when using their services. Some big data analytics, such as Cloud-analytics reduces the cost of storage of where they help some or- ganizations in storing high amounts of data from their company. Big data analytics helps organizations in speeding their executions since they pro- vide analytics, which is combined with the data sources; thus, they tend to have fast speed during the process of execution of their activities. Bug data helps in analyzing the activities that a user is making, and they end up giving a more accurate user result. Big data analytics contains semistruc- tured and unstructured data, which helps in encouraging internet click response, web server logs contents in social media, among others.( Ahmed, 2019) Big data impacts information technology, which includes; a relationship of the traditional database, which is not based on big data analytics deal with a high amount of data such as making the traditional databases a chance of looking for products in the market. New databases help in the process of managing the high amounts of data that flows in and out of the servers at a very high speed. Modern databases help in storing data, whether it is in the collection form or not, and it stores data that has no value in different folders of the server. Many organizations have to develop new databases that will be used in the process of managing the data that will be sent in the organization for the reasons for serving customers ef- fectively. To manage the modern databases, the information technology experts have to hire skilled personnel who will be capable of managing the database in case any hardware or software fails in the organization. Despite the information technicians being skilled, they have to collaborate in or- der to perform the desired task at the end of a task. In order for data management to be effective, components such as security, storage, and data reconciliation have to be installed for significant data analytics work well.

Prompt 3: discussion of ways that organizations can make their big data centers "green" Though data centers are not capable of producing waste materials, they have to make that the data centers are always green. For data centers to be green, they have to ensure that they do not mis- use electricity, water, and air in their centers. Big data have to operate efficiently, and they have to ensure that they have increased their rate of us- ing green energy. Some big companies such as apple Facebook and google are going green while they are utilizing their innovative ways, which are exceptional and fantastic in making the energy that they utilize green. (Ingle, 2017) In order for the organizations to meet their goals in promot- ing a green environment, they have to pressure the managers to use their information technology devices in a green manner that they are going to keep their environment safe. In order to ensure that they are utilizing low amounts of data, they have to calculate the amount of cost of the electric- ity that they have used with the price that they purchased the server that is in the organization. In order to conserve the current that metadata cen- ters are using, Microsoft, Google, and yahoo moved their metadata centers very close to river Columbia which was known for hydroelectric power in order to get the benefits of getting cheap power to their servers. In order for organizations to ensure that they keep the environment green, they can utilize renewable sources of power, which will allow innovative centers to use less amount of power. For example, in 2018 google purchased 3 gigawatts solar panel and wind stations that would help the organization in powering its data centers for a long duration of time if the electricity 8 8 8/15/2020 Originality Report https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c9e23675-d4be-4601-bf51-9c8a4837feda&course_id=_ … 3/6 Source Matches  (29 ) Student paper 100 % Student paper 91 % Student paper 100 % Student paper 65 % Student paper 70 % Student paper 63 % gg p pg pg g y from the hydroelectric generators were not working. (Sharma, 2017) In order for companies to save power, they will have to purchase systems that will not be capable of generating much heat. When an organization has systems that are capable of generating much heat, they will have to find cooling systems that will have to rotate at high speed in order to reduce the amount of heat that is produced by the innovative devices. If the innov- ative devices do not get enough air, they can face an overheating challenge, which can make the metadata centers to fail. If the organizations have reduced systems that are not capable of consuming a high amount of power, they can be in a chance of reducing the amount of power they use.

References Nadikattu, R. R. (2019). Data Warehouse Architecture–Leading the Next Generation Data Science. Rahul Reddy Nadikattu" Data Warehouse Ar- chitecture–Leading the next generation Data Science" International Journal of Computer Trends and Technology, 67(2019), 78-80. Sebaa, A., Chikh, F., Nouicer, A., & Tari, A. (2018). Medical big data warehouse: Architecture and system design, a case study: Improving healthcare resources dis- tribution. Journal of medical systems, 42(4), 59. Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868-1883. Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data analytics: A survey. Computers in Human Behavior, 101, 417-428. Pauleen, D. J., & Wang, W. Y. (2017). Does big data mean big knowledge? KM perspectives on big data and analytics. Journal of Knowledge Management. More, N. S., & Ingle, R. B. (2017, February). Challenges in green computing for energy saving techniques. In 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI) (pp. 73-76). IEEE. Shakeel, F., & Sharma, S. (2017, May). Green cloud computing: A review on efficiency of data centres and virtualization of servers. In 2017 International Conference on Computing, Communication and Automation (ICCCA) (pp. 1264-1267). IEEE. 3 12 7 7 13 13 13 13 7 14 6 6 15 16 1 Student paper DATA WAREHOUSE ARCHITECTURE 1 Original source DATA WAREHOUSE ARCHITECTURE 1 2 Student paper DATA WAREHOUSE ARCHITECTURE 3 Data warehouse architecture Original source Data Warehouse Architecture Data Warehouse Architecture 3 Student paper University of Cumberlands Original source University of the Cumberlands 4 Student paper 08/15/2020 Original source August 15, 2020 5 Student paper A data warehouse can be described as a collection of data marts that represents the data history that was involved in various operations that were utilized in the business. Original source A data warehouse can be defined as collection of data marts that repre- sents historical data from various operations in an organization 6 Student paper Sources of data components Original source Data Store Components 8/15/2020 Originality Report https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c9e23675-d4be-4601-bf51-9c8a4837feda&course_id=_ … 4/6 Student paper 77 % Student paper 78 % Student paper 62 % javatpoint 83 % Student paper 100 % Student paper 65 % Student paper 71 % Student paper 73 % Student paper 64 % 7 Student paper 1) Metadata component Original source The metadata component 1 Student paper This component is equal to the data catalog or data dictionary in the sys- tem that is used in database management. Original source Metadata is equal to a data dictio- nary or data catalog in the system of database management 8 Student paper In metadata, the dictionary data is stored in structures that are made of logical data structure. Original source In data, dictionary data is kept about structures of logical data 9 Student paper 2) Data staging component Original source Data Staging Component 10 Student paper Trends in warehouse data Original source Trends in the data warehouse 8 Student paper In the current world of technology, many trends are used regarding the execution of data in the warehouse. Original source In the present digital world, there are many trends regarding the data warehouse 11 Student paper The data lake is used in the process of processing data while they use efficient tools such as the Hadoop system, and Data lake is used in the process of utilizing the distributed storage. Original source Data Lake is utilizing distributed storage and processing with the use of tools such as the file system of Hadoop 8 Student paper discussion of ways that organiza- tions can make their big data centers "green" Original source Discuss ways in which organizations can make their data centers “green” 8 Student paper Some big companies such as apple Facebook and google are going green while they are utilizing their innovative ways, which are excep- tional and fantastic in making the energy that they utilize green. Original source Big companies such as Google, Face- book and Apple are going green with different innovative ways which are constructive and futuristic in the long run 8/15/2020 Originality Report https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c9e23675-d4be-4601-bf51-9c8a4837feda&course_id=_ … 5/6 Student paper 100 % Student paper 79 % Student paper 100 % Student paper 100 % Student paper 100 % Student paper 100 % Student paper 100 % Student paper 100 % Student paper 66 % goodstrat 63 % 3 Student paper Data Warehouse Architecture–Lead- ing the Next Generation Data Sci- ence. Rahul Reddy Nadikattu" Data Warehouse Architecture–Leading the next generation Data Science" International Journal of Computer Trends and Technology, 67(2019), 78-80. Original source Data Warehouse Architecture–Lead- ing the Next Generation Data Sci- ence Rahul Reddy Nadikattu" Data Warehouse Architecture–Leading the next generation Data Science" International Journal of Computer Trends and Technology, 67(2019), 78-80 12 Student paper Medical big data warehouse: Original source Medical big data 7 Student paper M., Wallace, S. W., & Wang, Y. Original source M., Wallace, S W., & Wang, Y 7 Student paper Big data analytics in operations man- agement. Production and Opera- tions Management, 27(10), 1868- 1883. Original source Big data analytics in operations man- agement Production and Operations Management, 27(10), 1868-1883 13 Student paper A., Hamid, S., Hashem, I. Original source A., Hamid, S., Hashem, I 13 Student paper T., & Ahmed, E. Original source T., & Ahmed, E 13 Student paper Social media big data analytics: Original source Social media big data analytics 13 Student paper Computers in Human Behavior, 101, 417-428. Original source Computers in Human Behavior, 101, 417-428 7 Student paper J., & Wang, W. Original source W., & Wang, Y 14 Student paper KM perspectives on big data and analytics. Original source Big Data Analytics 8/15/2020 Originality Report https://ucumberlands.blackboard.com/webapps/mdb-sa-BB5a31b16bb2c48/originalityReport/ultra?attemptId=c9e23675-d4be-4601-bf51-9c8a4837feda&course_id=_ … 6/6 Student paper 100 % Student paper 100 % Student paper 86 % Student paper 100 % 6 Student paper S., & Ingle, R. Original source S., & Ingle, R 6 Student paper Challenges in green computing for energy saving techniques. In 2017 International Conference on Emerg- ing Trends & Innovation in ICT (ICEI) (pp. Original source Challenges in green computing for energy saving techniques In 2017 In- ternational Conference on Emerging Trends & Innovation in ICT (ICEI) (pp 15 Student paper Green cloud computing: Original source Toward green cloud computing 16 Student paper In 2017 International Conference on Computing, Communication and Au- tomation (ICCCA) (pp. Original source In 2017 International Conference on Computing, Communication and Au- tomation (ICCCA) (pp