(Answered) Big Data Scalability and Efficacy

(Answered) Big Data Scalability and Efficacy

(Answered) Big Data Scalability and Efficacy 150 150 Prisc

Big Data Scalability and Efficacy

While this week’s topic highlighted the uncertainty of Big Data, the author identified the following as areas for future research. Pick one of the following for your Research paper:
1. Additional study must be performed on the interactions between each big data characteristic, as they do not exist separately but naturally interact in the real world.
2. The scalability and efficacy of existing analytics techniques being applied to big data must be empirically examined.
3. New techniques and algorithms must be developed in ML and NLP to handle the real-time needs for decisions made based on enormous amounts of data.
4. More work is necessary on how to efficiently model uncertainty in ML and NLP, as well as how to represent uncertainty resulting from big data analytics.
5. Since the CI algorithms are able to find an approximate solution within a reasonable time, they have been used to tackle ML problems and uncertainty challenges in data analytics and process in recent years.

Your paper should meet these requirements:
Be approximately four to six pages in length, not including the required cover page and reference page.
Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook.
Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

Sample Answer

Big Data Scalability and Efficacy

Data is an essential item in the current world since most of the operations and decision-making processes are guided by data availability. In improving the performance of organizations, the available data should be organized and analyzed for effective decision-making.  Big data analytics are becoming popular in academic and industrial fields with an increasing desire to understand the developments of huge datasets to accommodate the increasing data quantity. In handling big data, data management experts ought to understand the importance of scalability and efficacy of the current and existent data analytic techniques being applied for big data. Scalability of data is the maximum data storage cluster size that can guarantee a complete data consistency to develop a single valid version of stored data in the entire cluster, different from the redundant physical data copies. Scalability of the databases enables the database system to perform additional functions when allocated greater hardware resources. This paper discusses the importance of examining the scalability and efficacy of the existing big data analytics techniques.

Scalability and Efficacy of the Big Data Analytic Techniques

Scalability is a crucial element in extensive data examination that accommodates rapid changes in growing data within limited storage. The scalable data platform enables the information systems to accommodate potential data growth as data needs keep fluctuating and increasing. Scalability is important for big data examination and a machine learning framework in analyzing real-time data and large information sourced from the internet, web, and smartphones. The examination is attainable by exploiting the computing and storage amenities associated with high-performing computing systems and the computing cloud. Machine-generated data is presented in different formats, including weblogs and smart meters, sourced from different sources. Data is classified as structured and unstructured forms and is described by five main dimensions: capacity variation, speed, value, and accuracy. Analysis of big data involves examining the nature of data in determining the unknown correlations and hidden patterns. According to Wang et al. (2018), big data analysis requires substantive investment in both hardware and software resources.

Different big data analysis techniques should be examined empirically for efficacy and scalability. Data is widely described in terms of volume, speed (velocity), and variety of data which are the basic ways of establishing the nature of big data. Apart from computer-based data analytics, there are traditional statistical methods for analyzing big data. The way data analysis techniques function in an institution or organization is either through twofold – analysis processed through data streaming as it emerges and performing batch analysis as the data continues to build up.

A/B Testing is one of the major data analysis techniques used to establish meaningful data for decision making and knowledge building. The technique compares the control group against various test groups to establish what main changes or treatments can improve a particular variable. Big data can be tested using this method by testing huge numbers but can largely apply to bigger data sizes in gaining meaningful differences. Data fusion and data integration is another data analysis technique that analyses data from various sources and insights an extra effective and accurate compared to when from a single source. Comparing different sources improves the probability of attaining the correct decision for a solution.

Machine learning is another important technique for big data analysis, which is based on computer algorithms in producing assumptions based on the provided data. This is used in making predictions that can be impossible for human analysts to make. Data mining is another technique that involves extracting data patterns from big data sets through combined statistics and machine learning methods and within database management. The process of data mining involves extracting usable data from raw data by recognizing the hidden patterns and data from a huge data set. Other techniques include natural language process and statistical techniques, which use algorithms to analyze human language and collect and present data from surveys, respectively.

Empirically examining the scalability and efficacy of the data analysis techniques is valuable for maintaining consistency in big data analysis. The accuracy of the analyses techniques is one of the most important reasons the analytic data techniques are examined on the levels of scalability and effectiveness. Data analysis techniques are meant to ensure that they extract the most meaningful data from big data, and thus the data should be effective. The more the volume data, the higher the probability of coming across invaluable and inaccurate data. According to Hariri et al. (2019), big data increase uncertainties for big data analytics due to high levels of incompleteness, noise, and inconsistency. There are numerous sources of data that may not be accurate or relevant for various searches. The data analytic techniques samples from the big data to provide information that is useful in making decisions, and thus, if it collects inaccurate information, the users will end up making the wrong decisions. An empirical examination of the data analysis techniques compares the functionality of the analytic technique in different platforms to examine the accuracy of the techniques in collecting, analyzing, and presenting the data or predictions. The amount of data and variability of sources is increasing, and thus the analysis techniques should be examined on whether they maintain accuracy in scaling their capacities with the increased amount of data.