The Compute Infrastructures for Big Data Analytics

Introduction

The big data era is steadily setting in to firmly settle amongst us. Enterprises in its long and arduous journey are increasingly becoming data-driven in order to systematically zoom ahead of their competitors by smartly leveraging their streaming and accumulated data assets. These days every decision, small or big, is being taken based on data by individuals, innovators and institutions. The much-complicated and enterprise-wide strategy-making process solely depends on data. The persistence on the slogan “more with less” is bound to see a neat and nice reality with the sparkling and scintillating big data idea. There are concerted efforts underway in order to ensure and enable data availability, accessibility, accuracy, integrity, and confidentiality. The utility and usability of data are going up significantly while robust and resilient mechanisms for data capture, loading, storage, transformation, and transition data manipulation, analysis, filtering, cleansing, polishing, mining, and processing for knowledge discovery; information visualization and dissemination tasks are fast-emerging and maturing. On the positive side, the technologies and tools enabling the transition from data to information and to knowledge are really competent and captivating. The noteworthy advancements in the big data discipline are being taken note of by business executives, decision-makers and leaders in a positive manner. There is a close synergy among university professors and industry professionals in identifying inhibiting issues, perpetual drawbacks, limitations and barriers in order to come out with appropriate technology-sponsored solutions to speed up the adoption(Ahuja & Moore (2013)).

Heads of all kinds of business horizontals and verticals are purposefully planning and prototyping big data projects in order to fully understand the tactics, strategic advantages, any hidden risks, business, technical and user benefits, etc. towards framing and formulating a flexible and futuristic big data strategy. There is a greater awakening and articulation in embracing and encapsulating standards-compliant big data platforms, integrated processes, design patterns, evaluation metrics and best practices in order to reap all the originally envisaged benefits of the big data paradigm. There are a number of generic as well as specific big data applications and services emanating and evolving consistently based on the big data ideas from the development community.

The principal application of big data is nonetheless the big data analytics, which grandiosely promises extracting and exposing actionable and pragmatic insights from data heap for speedier decision-enablement and efficient actuation. The subject of data analytics in the big data era is a serious and strategic step which is not to be taken lightly by growing companies in order to be distinctive and dramatic in their offerings, operations and outputs. There are a multitude of reports from leading market analysis and research groups to substantiate the importance of the big data idea in propping up and propelling industry segments in their everyday activities. The much-anticipated business automation, augmentation and acceleration are bound to get a strong improvement and impetus with the maturity of big data products from highly accomplished and acclaimed product vendors.

In this chapter, I would like to highlight the infrastructural requirements for next-generation big data analytics. Especially the series of long-lasting infrastructural optimizations being represented by the raging cloud idea has a deeper impact on the big data analytics. This chapter dedicatedly details about the server infrastructures on public, private and even hybrid cloud environments.