Essay Example on Big Data Visualization: Unravelling the Beauty of the Large Data Sets

Introduction

I selected the topic of big data visualization. With big data, there is the identification of how we, as humans, interact with large amounts of information as every moment in time goes by. From this abundance in data, the needs to be appropriate ways to establish an understanding of all these big data. And as such, derive the beauty that comes with it. The topic is very appealing to me as big data visualization is one of the most effective ways to assuring realizations on the beauty of the large amounts of data. It involves the presentation of all information in any preferred graphical format. The primary target is to enable understanding of the data and be able to interpret as desired to establish meaning and trends relating to the kind of operations one is involved in usually.

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There are several established methods of visualizing big data. It is from simple techniques like a bar or line graph to more sophisticated methods like fever charts (Bikakis, 2018). Big data visualization then allowing for the scaling of these enormous amounts of data. Such is through the utilization of computers and other compatible devices with high capability levels where data is fed. A result would be the construction of representations in the forms selected by the operator. These graphical visuals are then observed and interpreted to deduce patterns and behavior of identified variables.

Importance of Big Data Visualization

It is essential to accept that the numerous existing organizations are continuously creating data at a high rate. They then desire to derive usefulness from the same data. The topic is relevant to a course on digital and social media. It is considering the continued growth in the number of social media users and data transacted over such platforms. Big data visualization has established as the most appropriate approach to assuring the relevance of data. Providing a better basis for all decisions making processes, as such big data visualization methods offer a very effective way to initiating various data-related operations.

Review Big Data

The forms in which large amounts of data can be presented in a unified structure with consideration of the purpose of reviewing the data. As such, the understanding of the desired concepts from this data is easier and less time-consuming. Big Data visualization established as the fastest approach to reviewing data.

Identify Trends

It has been a rather complex endeavor to identify the patterns of these massive amounts of data. With big data visualization, it is easier to compact data from the various relatable sources and establish the existing trends with such data (Bikakis, 2018). It is in association with developing baselines to any operational priorities in specific industries. Such assists in developing better levels of competitive advantage than other organizations based in a similar industry. Such trends identified as the most relevant source for observing the best opportunities to pursue during the business.

Identify Eelationships within Data

Big data visualization enables the identification of the existing correlative relationships between data from different sources. Offering better chances of reducing insight on the types and behavior of the various existing operational departments at a firm (Fiaz et al., 2016). It will be through the manipulation and changing of related processes as desired, attention then allocated to the major purpose of interpreting selected visual presentations. Such then relates to the decision on the most appropriate big data visualization technique to utilize.

Explaining of Data

The ease in interpreting these presentations equates to the easy presentation and explanation of observed data to workmates or senior managers. All identified patterns and behavior of data well visible to anyone, even those who lack previous knowledge on the particular aspects you are illustrating. They are effective for any presentation one desires to communicate with optimum levels of relevance.

Challenges to Big Data Virtualization

The use of traditional visualization methods has proved not so effective in handling these large amounts of data. It is considering that these data are changing over time with significant extents to their variabilities. Several advances have been attempted on these traditional methods to keep them up to pace with the developing world, but still observed to lag on efficiency. The primary priority in using these tools is to assure very low levels of latency. As such, there should be consideration of appropriate approaches to reducing latency. Such increases the complexities of utilizing these methods. The other challenge arises with the existence of semistructured data with the possibility of interacting with those that are completely unstructured. As such, there is a limitation on the most relevant tool for representing such data. The requirement of identifying the one that assured correctness to these forms of data also. The existence of such large amounts of data raises the need to implement related knowledge on parallelization. Such as establishing to present significant amounts of difficulty with regards to visualization. More precisely, it is hard to perform related visualization tasks independently, which is what the incorporation of a parallelization algorithm entails. Each presented problem that needs visualization then needed to be broken down into very independent operations that cannot be initialized concurrently with other tasks.

With visualization, the primary target is to understand the existing patterns and relationships existing within available data. As such, there arises the task of selecting data dimensions that are relevant to the visualization. Reducing these dimensions implies a reduction in the possibilities of developing interesting and useful patterns (Adeel, 2017). Even though the visualizations would then be lower as needed for utilizing less advanced visualization techniques. Another downside is existing with the use of all constituting dimensions. The visualizations would end up too densely concentrated, offering difficulty in interpretation by users.

The several readily available and easy to use tools possess low capabilities to scalability and assuring all expected functions of visualization. As such, large amounts of data increase the difficulty of performing needed visualization processes. Also, in every given data set, there exist several constituents that possess similarities thus can be easily identified as the same even when they are not. It is then a difficult task to distinguish and separate them (Wang, 2017). There are attempts to reduce the aspects of dataset visibility. Such actualized by increasing the lengths to response time while initiating selected operations. However, this can result in the loss of significant portions of all presented information (Wang, 2017). Also, with visualization, we are only limited to human interpretation. After observation, all deductions and conclusions entirely based on an individual’s perception of the appearance of represented data. Some techniques show frequent changes in the specifics to the images presented. A high rate of these changes can offer difficulty in understanding the behavior of data. Both sources used for this section, establishing to offer better insight from the concepts relayed. Such assured from the fact that they are peer-reviewed and accredited. Presenting a better understanding of the challenges encountered during big data visualization.

There are several examples of visualization tools. From the identified challenges with big data visualization, they have been constructed and developed to attempt to address these issues. The primary priority during development is to assure that the tool is interactive, which is easy for use by any visualization expert. All relevant information relevant for visualization must then be incorporated to be presented by these tools. The added functionality to zooming in or out as desired by a user. Such enables better precision during the interpretation of all visualized data. Also offering optimum levels of adaptability considering the possibility of selecting any particular set of data.

Tableau

This tool offers better chances to optimizing on the concepts of business intelligence. It is a better interactive virtualization tool with the provision of several options for performing any visualization operations. One is with the capabilities of customizing the tool as per their preference. The added advantage to flexibility and lesser time in providing image representations of inputted data. This tool also developed with the capacity to handle any data formats. Another function ability that allows its connection to various types of servers. An example of how users to Amazon Aurora can integrate tableau to their devices. The use of R also possible, which is a highly interactive programming tool.

Microsoft Power BI

It is a cloud-based analytics service that includes certain components of any cloud computing platform. From its title, all visualizations are rich. Also offering a highly interactive tool that allows for an easy understanding of its use. Three established sections are constituting a power BI. That includes the Power BI desktop, the Software as a Service platform, and the composition of apps relevant for performing identified visualization functions. All these components are readily available to the user. Such increases the levels of flexibility of the Power BI to configurations comfortable to any user and the types of data that they desire to visualize and analyze (Theys et al., 2019). The time to commencing needed visualization procedures taking rationally minimal amounts of time. The tool also provides a user with the capabilities of utilizing other Microsoft tools on the corporation with the visualizing properties already in existence. The feature that has created some sense of preference for the Microsoft Power BI is the added possibility of using an individual’s natural language to present any instructions or queries concerning any sets of data inputted to the system (Theys et al., 2019). The no requirement to be a programming expert to initiate desired operations using this tool. The abilities to combine several selected forms of data and combining them to a typical identifiable pile. Developing appropriate models to organizing and deriving relevance from data over automated processes.

PlotlyPlotly (also known as Plot.ly) is developed with the incorporation of python frameworks to Django infrastructure. It allows for the desired visualization with the addition of any data analytics procedures. There is an available application that can be owned and used for free. It, however, provides limited visualization and analytics functions. But any user can purchase the premium version that offers professional membership. When online, one can assess all resources to created charts from data and other functionalities that dashboards provide. It offers a wide variety of options to these creations, depending on the needs of a user (Theys et al., 2019).

References

Adeel, M. (2017). Big Data Virtualization and Visualization: On the Cloud. In Decision Management: Concepts, Methodologies, Tools, and Applications, 1436-1452. doi: 10.4018/978-1-5225-1837-2.ch066.

Bikakis, N. (2018). Big data visualization tools. arXiv preprint arXiv:1801.08336.

Fiaz, A. S., Asha, N., Sumathi, D., & Navaz, A. S. (2016). Data visualization: enhancing big data more adaptable and valuable. International Journal of Applied Engineering Research, 11(4), 2801-2804.