An article that helps one understand big data while offering key insights is by Alharthi, Krotov, and Bowman (2017), published in Business Horizons journal. Extensive data has always posed a significant challenge in the processing and managing data over time, bringing about changes in its collection and sampling techniques. One of the crucial factors contributing to this problem is the existence of sufficiently large and complex data sets that defy easy iterative management ( Alharthi & Bowman, 2017 ). Such large data sets cannot fit into a simple database network because their analysis requires too much work on the servers’ part to handle the data. Moore’s law, or the doubling of transistors on a circuit every two years, creating smaller hardware and data storage devices is a significant part of what made big data. This led to the increase of the computing ability of accessible software systems where personal computers could handle more substantial amounts of data, with business and vanguard systems being able to handle data sizes that were inconceivable a few years before.
The cost of acquiring an extensive data management system is more negligible than the cost of big data. This is attributed to the management and maintenance of such a system. The more the business feels the need to increase its operations, the more the additional costs begin to pile onto the original amount. A once 6 terabyte (TB) cluster may need to be vertically scaled upwards of 200 petabytes of storage space, handling nodes worth hundreds of thousands ( Alharthi & Bowman, 2017 ). It poses a bigger problem than paying for additional storage space and processing power. More infrastructure means more human capital, which brings us to the most variable cost of adopting a big data platform. The cost of developing a full-time Hadoop expert varies greatly depending on the developer’s experience, location, and size of the project.
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It is essential to leverage open source and managed big data platforms to alleviate the big data issue. The Google Big Query system that has seen mass adoption from traditional methods is meant to be an alternative to more expensive data silos and systems like Hadoop ( Alharthi & Bowman, 2017 ). The most remarkable difference with the Big Query system from all other systems is the pay-as-you-go business model. In the long run, one only ends up paying for server space and computing power consumed.
Reference
Alharthi, A., Krotov, V., & Bowman, M. (2017). Addressing barriers to big data. Business Horizons , 60 (3), 285-292. http://dx.doi.org/10.1016/j.bushor.2017.01.002
