DATA WAREHOUSE ARCHITECTURE, BIG DATA AND GREEN COMPUTING

Introduction.

 Below is a discussion that entails three relatable topics: Enterprise data warehouse, large data, and green information technology to help understand in a better way the trends and advancements in information technology. The three components also aim to show the inter-relationship between businesses and information technology as far as technology is concerned.

Prompt 1: Data Warehouse Architecture.

Enterprise data warehouse has mainly five warehouse components. These components have data warehouse characteristics such as; they are subject-oriented. The second characteristic of these components is that they are highly integrated. Time variation and non-volatility are also among the features of these components. The first component is the information warehouse record with the dominant database that is the basis of the information warehousing environment. Implementation of the history is conducted through a technology known as RDBMS. The RDBMS has been proven to be slow in its operations and new, improved technologies. Examples of the trend technologies in data warehouse databases are; usage of a multidimensional database (MDDBS) which helps to win challenges that are placed due to interpersonal data warehouse copies.

Another instance is new index edifices used to outdo interpersonal table scans and enhance speediness. The second component is sourcing, onslaught, acquisition, and conversion tools. These tools are also known as extract, convert, and cargo. These apparatuses do changes, summarizations, alterations required to convert information to a unified set-up in the information warehouse. The third component in the enterprise data warehouse is query tools. The query apparatuses help data users to intermingle with the information warehouse system. Also, the query apparatuses have four unlike groups: data mining apparatuses, OLAP apparatuses, reporting, query apparatuses, and application development apparatuses. The fourth constituent is information warehouse bus design that enhances knowledge of information flow in a given storeroom. This data flow is categorized as inflow, up flow, down flow, outflow, and Meta flow.

The last component of information warehouse design is information warehouse design best practices. Various practices are required to design a data warehouse architecture (Rifaie et al., 2008).  The first practice is the use of optimized data warehouse models to retrieve information. These models can be a hybrid approach, dimensional model, or DE normalized. The second practice is to choose a top-down and bottom-up approach in designing as they are appropriate approaches in the data warehouse. The third practice is the need to confirm data is processed faster and accurately. One should adopt a tactic that combines the information into a single form of honesty on data processing. Another practice is to build the information acquisition and cleaning procedure for information stores keenly.

Also, the designing of Metadata architecture enhances the distribution of metadata amid information mechanisms—consideration of applying the ODS type to assist in data recovery. Multiple sources of operations require to be accessed; another practice is necessary. Lastly, data models should be integrated as another effective practice. One should consider the 3NF data model to assist in the integration—the key trends in data warehousing data marts under the information warehouse bus design. The information mart is the trend that acts as a lesser of information warehouse and is used to divide information created for a specific target market. IT experts make data marts in a different database or the same data warehouse. Another trend in the data warehousing system is Metadata. Metadata is a high-level technological data warehousing valuable concept in maintaining, building, and managing the data warehouse. Metadata is also categorized into two groups that are practical Metadata and commercial Metadata. The technical Metadata has data that is used only by the administrators and designers. Then, the business Metadata has information used by end-users to enhance their understanding of data stored in the information warehouse. Metadata also shows a significant role in the information warehouse design as it shows the usage, source, and features of data in the information warehouse. Metadata are critical ingredients in the conversion of information to knowledge.

Prompt 2: Big Data

Big data is an extensive collection of information that still grows with time (Sagiroglu and Sinanc, 2013). Comprehensive data can also be said to be data that is collected from multiple sources. Extensive data is in three types. The first one is organized data. Structured information is highly classified information that is managed, kept, and recovered with ease. Unstructured data is the second type that includes data that lacks a specific form. Email is a perfect example of unstructured data as it takes a lot of time to process and analyzing this kind of data. Semi-structured is the last type of big data that has both organized and unstructured information in it. Semi-structured data states to information that, though not categorized data, has vital helpful information to the target specific audience. Big data use particular technologies like operations big data, big analytics data, Hadoop ecosystem, artificial intelligence, NoSQL database, R programming, and data lakes. These technologies enhance big data's growth, efficiency, and effectiveness. An example of big data is social media sites because many social media sites currently contain various sources. Social media sites are also increasing daily, and most people are adopting these sites for educational purposes, games and fun, business purposes, entertainment, and transportation purposes. Big data is being used professionally as a critical tool to manage social media marketing campaigns for different businesses. As an example of big data in the industry, social media provides immediate feedback from consumers of certain products as posted on social media platforms. This feedback is enhanced by sharing, commenting, liking, and following a specific product, and they help learn consumers' behavior and preferences on this platform. With the amount of data available from social media sites, big data is assisting businesses to manage this data and gain positive insights from their product users. Data has increased immensely, so small businesses and big businesses use big data for more innovative and constructive purposes, especially on social media.

As an example of big data in business, social media has saved marketers time and resources as the analytics have enhanced improved ad targeting. The ad targeting has impacted positive results of products advertised by managers. Also, I have experienced social media as an example of big data to be effective in communication. Communication has been enhanced by social media platforms as people in different geographical areas, whether locally or internationally, can get in touch with each other with ease. Various social media sites such as IMO, Skype, WhatsApp, Facebook, telegram, among many others, work efficiently and effectively to enhance that kind of touch. Big data is placing demands on organizations because the organizations need to adapt to trending analytics that empowers businesses. Organizations are required to be able to evaluate customer feedback as well as analyze changing tastes and preferences of their target customers. Also, companies should change to creation and innovations where they come up with new products and focus on the customer-driven services system, which will promote business growth. Whereas, under information technology management, big data demands a change of technology to more advanced technology. Big data requires the data management technology to create software that assists in data preparation and products that lead to data quality. Big data has significant emerging trends, mainly in technologies. Some emerging big data technologies include; Tensor flow enhancing the creation and deployment of powerful learning machine applications quickly. The second emerging technology in big data is the beam that offers a dense API layout that creates urbane parallel data that process pipelines through different performance runners. Docker is another emerging technology in big data that enhances more straightforward development and deployment of container applications simpler. Airflow, Kubernetes, and blockchains are also emerging technologies in big data that improve efficiency and effectiveness.

 

Prompt 3: Green Computing.

Green computing is an emerging trend in the current business world that enhances computers and computer resources' environmental responsibility and eco-friendly usage. Wells Fargo is an ideal example of an organization that has adopted green computing techniques successfully (Maiorescu-Murphy, 2020). Fargo wells engage in green computing best practices such as linking green teams to a corporate goal. In 2010, Fargo Wells adopted a formula of combining internal communications with a permanent brand to get all employees using their major business portals. This adoption assisted the organization in reducing and recycling waste, paper-reduction, and commuting options. The second green computing practice that Wells Fargo adopted to enhance green computing is posters and outlines with an established brand. Wells Fargo ensured that all teams presented the greenhouse gas emissions to improve the energy reduction campaign. This campaign was enhanced by the use of a consistent brand to reach all employees in the organization. The other practice that Wells Fargo engages in to promote green computing is engaging its consumers. The organization created a forum where their customers are educated on the environmental industry, thus enhancing green computing. The other practice wells Fargo confronts links their volunteer employees to environmental priorities to promote green computing in their firm. Wells Fargo has a culture of volunteerism. Hence, the organization used this culture to promote green computing in their organization and recognize achievements and employee participation in the last practice Wells Fargo has adopted to enhance green computing in the organization. To enhance this green computing practice, the organization commissioned an artist who made a reward trophy out of recycled textiles used to reward green team accomplishments. This art strategy was very effective in Wells Fargo in raising awareness and promoting green computing. These practices in Wells Fargo have enhanced energy conservation, among other benefits regarding green computing in this firm. Organizations can make their data centers green by conducting a baseline energy audit. Organizations should start conducting this audit as it provides feedback used for future assessment in long-term goal planning. Also, the baseline energy unit offers an evaluation of usage and efficiency in the organizations.

The selection of green-friendly materials and environmental attributes is another way organizations can make their data centers green. When organizations use locally sourced and renewable sources in building cold locations to benefit from free cooling and in their power servers, they save a lot. Organizations should also prioritize the reduction of data center and power usage to make their data centers green. This reduction is achieved by lowering the amount of energy needed to start the IT equipment. When data managers focus on combining and picturing workloads and eliminating powered servers that are dormant, they decrease power drain. Another way in which organizations make their data centers green is by designing modular data centers. Organizations achieve this design by adopting the pre-fabricated approach that minimizes the organizational goals and increases the predictability of the build process. Modular data centers enhance the organization to become more flexible in satisfying their computation demand. The final way in which organizations can make their data centers green is through optimizing data center cooling. Datacenter managers can use the advantage of building data centers in naturally cold areas by installing outside air economizers instead of using a power source for cooling. Also, isolation structures generate a lot of heat in other parts of the building as they contain data center equipment. Building a green data center is initiated by identifying and facing the inefficiencies in the organization's development (Saha, 2014).

Conclusion.

Data warehouse architecture is very useful as it helps businesses to survive through the provision of relevant historical data from sales, stock data to staff and intellectual property records. Enhancement of quality data and conformation ensures that data produced by different business divisions has high quality and standards. Data warehouse architecture also offers a competitive strategy by enhancing metric-based decisions to major sales levels.

 Big data is also very useful as it is growing because it helps organizations and information technology experts be on their toes to match the trends that come with big data. Big data also plays a major role in connecting personal relationships and business relationships among different fields. Research has shown that if big data is used effectively and efficiently in all fields, the economy will make big positive strides worldwide. Social media platforms, more so, have great potential in the development of better businesses.

Green computing has also been proved as equally relevant in the current world of business. When adopted by organizations, it assists them in saving a lot of power and fuel in their data centers. Green computing has also posed a major challenge to organizations as the organizations will have to upgrade their technology to adopt green data centers. An organization like Wells Fargo has also posed positive challenges to other organizations as they are doing exceedingly better due to adopting green computing in their data centers (Maiorescu-Murphy, 2020).  Green computing has also enhanced the conservancy and effective use of natural assets. Above all, green computing has proved cost-effective from the less energy usage and the cooling requirements.

OUTLINE

DATA WAREHOUSE ARCHITECTURE, BIG DATA AND GREEN COMPUTING

Thesis statement: The three components also aim to show the inter-relationship between businesses and information technology as far as technology is concerned.

• Introduction
• Prompt 1: Data Warehouse Architecture.
• Prompt 2: Big Data
• Prompt 3: Green Computing.
• Conclusion