A growing number of social networking sites enabling the formation of digital communities in knowledge,business, healthcare, and scientific domain is generating huge volumes of structure, semi-structure,and unstructured data with lots of hidden meaning and value. Mobile Cloud computing has enabled IT applications to make this data accessible anytime, anywhere, to anybody, any device, and any application.The data generated by various social networking communities, sensor generated data, Machine-toMachine (M2M) communication, and IoT has resulted in the demand for high speed in-memory computing for real-time processing and data analytics for systematic knowledge discovery, dissemination,accurate decision making, and business planning. These developments and requirements have individually as well as collectively laid a stimulating foundation for crafting several hitherto unknown things in business as well as IT domains. In this book, we have discussed various facets of big data analytics and the related technology.
Social networking and big data has opened fresh possibilities and opportunities for business organizations.Newer and nimbler service-oriented, Cloud-based, insights-driven, knowledge-intensive, multienterprise,cognitive, and collaborative applications are emerging and evolving in order to catch up with the varying business volume, value, and veracity of big data. Knowledge extraction and engineering to design, develop, deploy, and deliver smarter applications and services is to become common, casual,and cheap. There are highly valuable and visible benefits in the form of simplified, streamlined, and synchronized business and IT processes. Some of the perpetual problems such as IT sprawl, a lingering disconnect between business and IT, the lack of business and IT agility, etc. are getting resolved, as the integration scope {Device-to-Device [D2D], Device-to-Cloud [D2C]. Cloud-to-Cloud [C2C], Enterpriseto-Cloud [E2C]) gets widened significantly, and real-tillle, predictive, and prescriptive big data analytics are set to become pervasive and persuasive eventually.
The tremendous infrastructural requirements for next-generation big data analytics is being tackled with a series of remarkable infrastructure optimizations (IT simplification, rationalization, sharing,virtualization, higher utilization, consolidation, convergence, and automation) being facilitated by the raging cloud idea. Many more disruptions and transformations are needed for a nice and neat reality to unfold with the faster proliferation of cutting-edge technologies, versatile processes, path-breaking architectural patterns, and state-of-the-art infrastructures.
Analytics as a Service {AaaS) is the focus area of a large number of IT solutions providers for offering Cloud-based big data analytics solutions. Some of the leading AaaS is introduced in the book for the benefit of the readers. We have also explained at length all the notable transformations these technologies are to bring in for the total human society and the various kinds of tactical as well as strategic implications. We have described a number of business challenges and concerns and how they can be overcome with the incoming innovations and improvisations. We have written the distinct and decisive advantages (business, technical, and use cases), how the dreamt knowledge society is to see the light with the subsequent maturity and stability of these innovations, and what the future holds for us.
In the first chapter, "The IT Readiness for the Digital Universe," we have indicated the emerging and evolving trend of the digital universe, the resulting big data, how the next-generation IT infrastructures need to be optimized, and finally, how the challenges of the digital universe can be comprehensively tackled through Cloud infrastructures.
In the second chapter, "Big Data Computing and the Reference Architecture," there is a detailed description about big data computing and the reference architecture with the aim of clarifying what exactly big data computing is and how it is contributing for the stagnating Business Intelligence (BI) domain.We have detailed the reference architecture in order to tell about the various contributing components to the worldwide developers of new-generation big data applications.
The third chapter, "Big Data Analytics Demystified," unravels the mysteries of big data analytics. There are briefs about the various components in the fast-moving big data analytics discipline. The enabling technologies, the key drivers for big data analytics, how it is being accomplished, what the platforms and tools facilitating the complicated task of big data analytics are, the impending challenges, the need for best practices based on real-world projects, etc. are all described in this chapter.
The fourth chapter, "The Compute infrastructures for Big Data Analytics," talks in detail about the various compute modules that are needed to design, develop, deploy, and deliver big data analytics applications.
In the fifth chapter, "Storage Wrastructure for Big Data and Cloud," examines the various storage technology options that are available and their suitability for storing big data. There is a detailed description of Cloud-based big data storage. There is a brief on storage virtualization and how it benefits next-generation big data storage.
In the sixth chapter, "Big Data Architecture: Storage and Computation," the author has clarified and answered several pertinent questions. With the unprecedented increase in the data sources, the question remains how to collect them efficiently, effectively, and elegantly, store them securely and safely, how to leverage those stocked, polished, and maintained data in a smarter manner so that industry experts can plan ahead, take informed decisions, and execute them in a knowledgeable fashion.
The seventh chapter, "The Network infrastructures for Big Data Analytics," explains the various network components that in turn help in formulating a compact and comprehensive Cloud center infrastructure for performing big data analytics.
The eighth chapter, "NoSQL Databases," talks about the need for the recent phenomenon of NoSQL databases. Their unique capabilities and contributions towards big data analytics are described here. The various NoSQL database solutions are detailed in order to throw light on what stands where.
In the ninth chapter, "Cloud Databases Systems: NoSQL, NewSQL, and Hybrid," we have covered various Cloud databases (on-premise and off-premise). There are briefs about the traditional SQL databases in Cloud environments and the recent entrants of NoSQL, NewSQL, and Hybrid databases that individually as well as collectively contribute to big data analytics.
The tenth chapter, "Driving Big Data with Hadoop Technologies," talks about the various big data analytics technologies. Primarily, it deals with the most popular software framework (Hadoop) for simplifying and streamlining big data analytics.
The eleventh chapter, "Integrating Heterogeneous Data for Big Data Analysis," describes the need for data virtualization technology in order to seamlessly and dynamically collect distributed and disparate data from multiple sources and integrate them for enabling big data analytics. There are multiple ways and means for data integration. However, in the distributed world, the data virtualization mechanism scores well over other methods. This chapter insists on the viability of leveraging the data Virtualization technology for data integration.
The twelfth chapter, "An Overview of the Virtualization Technology," explains the various types of virtualization, the leading virtualization platforms and products in the market, the unique contributions of virtualization for Cloud as well as big data computing, and how virtualization affects the total IT stack and brings in big benefits for big data analytics.
The thirteenth chapter, ''Data Visualization: Creating Mind's Eye," indicates the need for data visualization technology solutions for disseminating the knowledge obtained through big data analytics.The various methods for creating data reports, maps, charts, and other outputs for providing real-time information to authenticated people are described here.
The fourteenth chapter, "Significance of In-Memory Computing for Real-Time Big Data Analytics,"details what in-memory computing is, why it is very important for next-generation business applications,bow it speeds up the data processing, and its unique contribution for real-time big data analytics.As there arc more machine-generated data compared to man-generated data, the relevance and role of in-memory computing is simply phenomenal.
The fifteenth chapter, "Big Data Predictive and Prescriptive Analytics," is all about the emergence of next-generation analytical methods for predicting what is to happen in the near future and to assist executives on bow businesses need to be proactive and preemptive in understanding and strategizing the best course of action.
The sixteen chapter, "A Survey of Big Data Analytics Systems: Appliances, Platforms, and Frameworks,"brings out the popular products and platforms that are helping out IT industry professionals immensely in architecting, constructing, and deploying big data applications by automating both the generic as well as specific tasks associated with the complicated big data analytics.
The seventeen chapter, ''Middleware for Preserving Privacy in Big Data," expresses the emerging big data security challenges and competent solutions. With big data computing emerging as a powerful paradigm, it is logical that it would be subjected to a volley of security threats, risks, and vulnerabilities.This chapter has all that is needed to combat both the visible as well as the hidden security and privacy issues.
The eighteenth chapter, "Accessing Big Data in the Cloud Using Mobile Devices," illustrates the emergence of accessing big data-induced insights via a host of new-generation mobile devices. As big data is increasingly stored, managed, subjected to a host of analytical functions, the anytime, anywhere,any device access, usage, and leverage of real-time knowledge is paramount for executives on the move.
The nineteenth chapter, "Medical Data Analytics in the Cloud Using Homomorphic Encryption,"vividly explains the utmost security of medical data while in transit, in persistence, and in usage in the Cloud using an innovative encryption mechanism.
The twentieth chapter, "Bioinformatics Clouds for High-Throughput Technologies," insists the need for Cloud infrastructures for appropriately and cost-effectively performing bioinformatics analysis of molecular sequences.
The final chapter, "Green Cloud Computing: Data Center Case Study," illustrates the importance of energy efficiency while designing and operationalizing data centers for big data analytics. There are a number of energy conservation and preservation techniques and best practices in this chapter.

Chapter 1 The IT Readiness for the Digital Universe 1

Chapter 2 Big Data Computing and the Reference Architecture 22

Chapter 3 Big Data Analytics Demystified 38

Chapter 4 The Compute Infrastructures for Big Data Analytics 74

Chapter 5 Storage Infrastructure for Big Data and Cloud 110

Chapter 6 Big Data Architecture: Storage and Computation 129

Chapter 7 The Network Infrastructures for Big Data Analytics 157

Chapter 8 NoSQL Databases 186

Chapter 9 Cloud Database Systems: NoSQL, NewSQL, and Hybrid 216

Chapter 10 Driving Big Data with Hadoop Technologies 232

Chapter 11 Integrating Heterogeneous Data for Big Data Analysis 263

Chapter 12 An Overview on the Virtualization Technology 289

Chapter 13 Data Visualization: Creating Mind's Eye 322

Chapter 14 Significance of In-Memory Computing for Real-Time Big DataAnalytics 352

Chapter 15 Big Data Predictive and Prescriptive Analytics 370

Chapter 16 A Survey of Big Data Analytics Systems: Appliances, Platforms, and Frameworks 392

Chapter 17 Middleware for Preserving Privacy in Big Data 419

Chapter 18 Accessing Big Data in the Cloud Using Mobile Devices 444

Chapter 19 Medical Data Analytics in the Cloud Using Homomorphic Encryption 471

Chapter 20 Bioinformatics Clouds for High-Throughput Technologies 489

Chapter 21 Green Cloud Computing: Data Center Case Study 508

Compilation of References 534

About the Contributon 560

Index 566

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