components of data warehouse architecture and big data
Introduction
Modern-day organizations have adopted the use of the dynamic volumes of data to enhance precision and predictions in analytical work. The institutions are equipped with specialized tools and processes that aid in data transformation and cleansing. Though “big data” has grown to be an essential business element, many fail to acknowledge the challenges institutions face when handling data, along with the impacts they cause on the environment. This paper encapsulates the components of data warehouse architecture and big data, along with evaluating various trends associated with the technologies. Further, the paper will address the technique of green computing, citing its relevance in modern-day business environments.
Data Warehouse Architecture
A data warehouse is a typical management strategy that collects data from various stations across a network, as a data analysis tool. The systems were first developed in the 1980s to fill information gaps left by online application systems, that lacked significant cross-platform integration. Further, the systems were built to discard historical data periodically, which translated to information shortages. In this context, data warehouses were developed as sources of data, which were integrated, time-variant, and stable, to enhance decision-making processes (Watson, 2001). Don't use plagiarised sources.Get your custom essay just from $11/page
Data warehouse architecture provides an integrated database platform. Technically, a data warehouse doesn’t generate data, but rather it collected information from external resources, databases. Data from various resources enters the system, where it is cleaned, manipulated and stored. Data has to be transformed because of large volumes and diverse structures of information. Data warehouses also contain various tools that aid in data manipulation and cleaning (Watson, 2001). The process of transformation entails filling data gaps, data conversion, and deleting obsolete elements.
Forms of Data Transformation
Data transformation entails cleansing and assorting datasets in preparation for storage or analysis. In the context of architecture, data transformation can take two forms. The first approach is known as multistage transformation and it entails routine extraction, manipulation, and loading of data. Data is first extracted from resources, then it’s exposed to transformation in a staging area, and finally it’s loaded into the warehouse. Second, in-warehouse transformation begins with extraction, followed by loading, and transformation, respectively. Information is collected from resources and it’s loaded into the warehouse, where its subsequently transformed. Today, most systems use in-warehouse transformation because of the excellent performance of contemporary database engines. Further, most in-warehouse transformations are created in the SQL language, which is predominantly used by many analysts.
Trends in Data Warehousing
Contemporary firms and institutions depend on knowledge-based systems, which capitalize on data analysis. Furthermore, most knowledge-based systems incorporate data warehousing, as an integral element. Data warehousing programs are, in turn, integrated with analytical processing tools to enhance strategic decision making. Data warehousing is also prone to facing changes and trends due to the dynamic nature of technology. One of the current trends the field faces is in warehousing appliances. Firms often face the dilemma of making or buying appliances, but most opt for sourcing from specialized vendors. Manufacturers build and configure the devices, making sure they balance hardware, software, and services, to assure efficient performance. Buyers enjoy the aspect of simplicity as they avoid the struggles of reconfiguring appliances in cases of underperformance.
Another trend in data warehousing is the use of in-memory database management systems (DBMS). The technology executes prompt query response and data transformation, along with enabling the processes of analytics and transactions in a single database. DBMS systems can also be integrated with business intelligence solutions, to enable operations with high-end products. The systems will attract enterprises, specifically large vendors. Third, data warehouses can also provide cloud services, which enhance organizational data management. The systems provide an online platform that entails information management, data collection and analysis, and storage. The warehouse provides homogeneous information by gathering data from multiple databases, which eases the analysis process.
Big Data
Definition
Organizations operating in the digital age are amidst a period of data explosion, which brought up terms like “big data.” The phrase refers to inordinately mammoth volumes of data which are generated regularly, and on a global scale. Subsequently, firms are overwhelmed by the manipulation and storage processes of large volumes of data. Big data originates from rapidly growing sources like internet clicks, online transactions, user content, business transactions, and social media activity.
Similarly, fields like medicine, engineering, finance, and operations management add on to the dynamism of contemporary data. The sectors require robust data computation techniques and tools to reveal patterns across broad socioeconomic domains (Pearlson, Saunders, & Galletta, 2020). Data collected through such tools is used to complement archival sources, that are relatively static, thereby filling information gaps.
Technically, the term “big” refers to the mammoth size of datasets. However, researchers often associate big data with the phrase “smart,” in the context of the insights the data produces (George, Haas, & Pentland, 2004). Essentially, the definition focuses more on the nature of the data, rather than the number of subjects in the system. For instance, a race car generates numerous datasets from its sensors, which aid in analyzing the automobile’s components, along with the driver’s behavior and the general performance in a race.
Trends in Big Data
Big data in itself is a trend in the current technology era, with the global emphasis on business analytics, digital life, and efficient working environments. Organizations are evaluating various strategies through which large-volume data, can be utilized to realize value for individuals, firms, society, and governments. Big data can be deployed through techniques like web analytics, which predict statistics like consumer behavior, network traffic, and search patterns. Gradually, big data is growing from a pattern-analysis tool, to incorporate complex functions like predicting the occurrence of various events.
Green Computing
Definition
The current global environment faces numerous changes, like weather patterns, due to issues like global warming, industrialization, deforestation. Consequently, the planet faces issues like the melting of snow and subsequently greater sea levels. Similarly, the world has also seen an increased use of technology and computers, which require massive amounts of power to run. The energy requirements needed to run computers and related infrastructure pose a significant threat to the environment.
Therefore, green computing entails enhancing environmental sustainability, specifically by addressing the design, production, and utilization of computers and related infrastructure. Green computing 1.0 entails stressing on reengineering devices and processes to enhance energy efficiency, towards environmental protection. On the other hand, green computing 2.0 encompasses reengineering and enhancing supply chains, production, and organizational efficiency to maintain environmental standards (Saha, 2018).
Implementation of Green Computing in Organizational Processes
Google is a successful pioneer of environmental sustainability, mainly due to its numerous projects targeted at reducing waste and carbon footprint. One notable project entails collecting and analyzing data, which promotes the use of sustainable material, along with encouraging firms to cut emissions. Google also implements energy-saving techniques in its data centers. Further, the corporation invests significantly in renewable energy projects around the globe.
Additionally, Google runs a database project that is dedicated to the environmental impacts of manufacturing materials. The corporation uses a decentralized strategy to find practical approaches for reducing emissions and waste while boosting organizational productivity. For instance, the company has a team dedicated to investigating the impacts of machine learning on environmental degradation. Project leaders at Google are required to develop long-term strategies, along with assuring the effectiveness of sustainability initiatives.
Conclusion
Data warehouses were built as integrated sources of data, which were also time-variant, and stable, to improve organizational decision-making. The central function of a data warehouse is data transformation, which entails cleansing and assorting datasets in preparation for storage or analysis. The systems are particularly useful in the digital age, where organizations handle massive volumes of big data, which emanates from rapidly growing processes like business transactions, internet clicks, and social media activity. Despite the analytical benefits of big data, the processes and infrastructure involved in handling information, have catastrophic effects on the environment. Therefore, organizations are growing to adopt the green computing technique, which emphasizes environmental sustainability.
References
George, G., Haas, M. R., & Pentland, A. (2004). Big Data and Management. The Academy of Management Journal, 57(2), 321-326.
Pearlson, K. E., Saunders, C. S., & Galletta, D. F. (2020). Managing and Using Information Systems: A Strategic Approach, 7th Edition. Hoboken, New Jersey: Wiley and Sons.
Saha, B. (2018). Green computing: Current Research Trends. International Journal of Computer Sciences and Engineering, 6(3), 467-469.
Watson, H. (2001). Recent developments in data warehousing. Communications of the Association for Information Systems, 8, 1-25.