Data warehouse architecture big data and green computing

Data warehouse architecture big data and green computing

Introduction

With the increased technological innovations and inventions, there has been the introduction of new concepts and these are data warehouse architecture, big data, and green computing. Data warehouse refers to the source of data for an organization. It is the components in the warehouse that sum up to become big data, which is the output of various segments in the organization or entity. Green computing, on the other hand, deals with efficiency in resource use in carrying out the various commands as far as managing the database is concerned. This paper aims to outline the major components of the data warehouse while highlighting the key trends related to it. Apart from that, this paper will also cover my understanding of big data and the demands that such places on organizations. Last but not least, the manner in which organizations can be made green with an example of an already green organizations will also be captured in this paper.

Prompt 1: Data warehouse

Data warehouse architecture has various components. One of these components is the metadata. These elements provide a description of the data warehouse. Metadata revolves around aspects of building, maintaining along with managing the data warehouse. The most important aspect of this major component is that it provides users with interactive access, thus helping them to understand the content as well as find data. The other critical importance of metadata in a data warehouse is that it can integrate and transform the data in the warehouse while controlling its movement.

The other major component of the data warehouse architecture is the access tools. One of these tools is the query and reporting tools. The query tools, as the name suggests, are important inputs in addressing all the concerns that may arise from the users within the system (Inmon, & Krishnan, 2011). On the other hand, when it comes to the reporting tools as the other forms of access tools, this focuses on generating reports from the data that is stored in the data warehouse.

Data mart is the other major component of the data warehouse architecture. The data marts are usually taken as an alternative to the data warehouse and are known to take less time and money to build them. These components act as a data store that is subsidiary to the data warehouse. It should be noted that data mart can take the form of summarized or aggregated data.

Besides that, an information delivery system is the other important component of a data warehouse architecture. The latter stated component helps in information processing by having it delivered from one or more destinations. In other words, the information delivery system is mainly used in the distribution of data stored in the warehouse. The delivery is usually predetermined in that it may happen at a specific time of the day or on completion of a given event.

Data warehouse database is another critical component of the data warehouse architecture. This is usually regarded as the cornerstone of the data warehouse architecture and its related environment. The database warehouse is usually implemented on a relational database management system technology (Inmon, & Krishnan, 2011). However, it should be noted that the implementation is often constrained for transactional database processing.

The other important component of a data warehouse architecture is the sourcing, acquisition, clean up along with transformation tools. The latter stated input is instrumental in the extraction of data from the operational systems within the database while putting it in a format that is useful in applications that run off the data warehouse. The sourcing, acquisition, and clean up tools help in the conversion, summarizing, and making any necessary changes to the available information for purposes of decision making (Pedersen, & Wilkinson, 2019). To this end, it can, therefore, be stated that the various transformations that are relevant in the preparation of data in a data warehouse are sourcing, acquiring, cleaning up, and transforming.

Conversely, there are various trends that are associated with data warehouse and these are the use of managed services. The use of the latter stated services are important in creating possibilities that aim at reducing costs. The other emerging trend in the data warehouse is data marts for production lines. To enhance accuracy and provide clarity in the various organizational segments, there is a need to analyze data for different production lines. Last but not least, the other important trend in data warehousing is the use of columnar storage. The utilization of this technique helps in the improvement of disk performance.

Prompt 2: Big data

Big data is a concept that analyses and systematically extracts information from data sets that are too complex or rather large to be dealt with by traditional methods of data analysis (Baesens, 2014). This concept can be applied in both small and large organizations, although it is more relevant and useful in large organizations. Big data has numerous applications in quite a number of industries to include the banking and securities industry, communications, media and entertainment, education, and even healthcare.

For the case of the banking and securities industry, the concept of big data has been found to be useful in the monitoring of the activities of the financial markets. The use of the concept has been instrumental in the tracking of illegal trading activities in financial markets and this is enhanced through the incorporation of network analytics. Besides that, big data analytics is also useful in the banking and securities industry on the aspect of risk management, and this has been evident whereby it has been widely applied in fraud mitigation along with anti-money laundering.

Apart from the banking and securities industry, the application of big data is also evident in communications, media, and entertainment. The big data concept has been instrumental in the creation of content, recommending content, as well as measuring the performance of the content (Baesens, 2014). Besides that, big data has also been useful in making recommendations on the individual performance of entities in the music and entertainment industry.

Nevertheless, to effectively implement the big data concept, there are some demands that this places on organizations. One of the demands is with regard to employee training. With the introduction of big data concepts in organizations, employees have to acquire skills in data analytics. Apart from that, the other demand that is placed in organizations as far as big data is concerned is the need to acquire new state-of-art technology that is compatible with the innovation.

Apart from big data, there is also the concept of data management technology and this is a range of techniques along with database systems that are used in managing information and allocating access in business entities. This phenomenon has been of great impact on the global economy and this is by improving the operations. Besides that, data management technology has been instrumental in that it has helped in lowering the costs of doing business.

Prompt 3: Green computing

Data centers are one of the main cores of the operations of any organization. Without such components, it becomes hard for the organization to come up with sound decisions in its various organs. However, there is a need to have green data centers to ensure that there is efficient utilization of the available resources. One of the ways in which the organizations can make their data centers green is by conducting a baseline energy audit. This kind of move helps in providing a real-time assessment of usage and efficiency. Apart from that, the audit surveys will also be useful in outlining the impact of green data centers on total energy usage.

Besides that, the other way that can be used to enhance green data centers in organizations is by selecting green-friendly materials and environmental attributes (Harris, 2008). Some of the environmental attributes that are put into consideration in the implementation of green computing in the organizations include waste recycling, low emission along with the obvious energy saving. Besides, organizations usually save by using locally sourced materials as this is associated with low costs.

Apart from that, the optimization of data center cooling can also help in attaining green data centers in organizations. This can be done by installing air economizers that draw from the natural environment rather than having a power source meant for cooling. The optimization of air conditioning by the use of an alternative source can serve a great deal in power usage. Besides, there can be the installation of isolation structures that can generate most heat.

One of the organizations that have been able to successfully implement green data centers is the Apple Company Inclusive. This firm uses 100-acre solar farms that have numerous fuel cell electricity generator that uses biogas which is collected from nearby landfills.

Conclusion

In conclusion, data warehousing, big data, and green computing are some of the most applicable concepts in the current era of computing. With the application of data warehouse architecture, organizations are able to source and acquire data from all organs of an entity. On the other hand, when it comes to big data, this is usually applicable in the analysis of data. Last but not least, when it comes to green computing this is aimed at attaining efficiency in the organization, especially on the resources within it. An example of an organization that has been found to successfully implement green computing is Apple company Inclusive.

References

Baesens, B. (2014). Analytics in a big data world: The essential guide to data science and

its applications. Hoboken, N.J: John Wiley & Sons.

Harris, J. (2008). Green IT, 100 success secrets: Green computing und green IT best practices

on regulations and industry initiatives, virtualization, power management, materials, recycling and telecommuting. New York: Emero.

Inmon, W. H., & Krishnan, K. (2011). Building the unstructured data warehouse.

Bradley Beach, NJ: Technics Publications.

Pedersen, J. S., & Wilkinson, A. (2019). Big data: Promise, application and pitfalls