Deeper connectivity, Deft Analytics, and Decisive Insights

It is an overwhelmingly accepted fact that the next-generation systems are to be insights-driven to be innately smart in their operations and offerings. The futuristic systems are to be knowledge-filled to be extremely yet elegantly autonomic in their decisions, deeds, and deals. All kinds of systems (physical, mechanical, electrical, electronics and IT) in our midst are adequately empowered with more succulent automation capabilities through data-driven insights. Thus without an iota of doubt, data becomes the new fuel for future. The need for cognitive analytics also goes up significantly to work on those data heaps in order to extract actionable and adroit insights. The timeliness and trustworthiness of knowledge being discovered and disseminated are also being ensured through a host of technologically sound solutions to take precise and perfect decisions in time and to act upon that with all the clarity and confidence.

There are a couple of existential and essential requirements to produce smarter and sophisticated systems in massive quantities. Firstly, the squeezing in a number of next-generation features, functionalities, and facilities inside every kind of IT infrastructures, communication networks, handhelds, wearables, portables, mobiles, and fixed systems through the smart instrumentation at the design stage itself has to be greatly simplified and accelerated. The direct result out of this disruption and transformation is that we will be bombarded with scores of adaptive equipment, wares, kitchen utensils, robots, drones, rigs, consumer electronics and avionics, medical instruments, machines at manufacturing floors, appliances, etc. Precisely speaking, everyday systems getting intrinsically instrumented and interconnected to be intelligent are the most sought-after things in the days ahead. There are single board computers (SBCs), disposable and disappearing elements, a growing family of edge technologies (highly miniaturised sensors, actuators, chips, controllers, codes, tags, stickers, LED lights, beacons, specks, etc.), wireless gateways, device middleware, etc. in order to systematically transition dumb, closed, and inflexible articles into open, flexible, programmable, and remotely monitorable, measurable and manageable artifacts. That is, every common, cheap and casual thing in our personal, professional and social environments becomes productively digitized and succulently smart.

Secondly, everything has to have the much-insisted external connectivity, communication and collaboration capability. That is, any purposeful interactions, collaborations, corroborations, and correlations among connected devices, digitized and sentient materials are to result in a lot of usable and reusable data. In addition, all these ground-level devices are getting hooked with remotely held cyber applications. Now with software-defined clouds emerging as the one-stop, highly scalable, available, adorable, affordable, automated, and composable IT solution, there are increased focuses on seamlessly and spontaneously integrating everything with software applications, various enablement platforms, different analytic engines, middleware solutions, and databases getting hosted on private cloud, public cloud, hybrid cloud, and edge / fog / device cloud environments to bring forth hitherto unknown and path-breaking possibilities and opportunities. Resultantly there will be billions of connected devices and trillions of digitized elements and smart materials in the near future. The most game-changing applications being thought through and worked include connected vehicles, healthcare, energy, utility, cities, buildings, retail, supply chain, manufacturing, etc.

Thus with the faster emergence and convergence of a bevy of promising and proven technologies and tools, the data size getting generated, garnered, and subjected to a variety of intense investigations is growing exponentially. Further on, the data structure, scope, and speed are also rapidly changing towards producing better and bigger services for the humanity. It is being projected that there will be millions of software services due to the uninhibited advancements and noteworthy developments in the IT space. These services can be publicly discoverable, accessible, usable, and composable through a dazzling array of automated tools to design, develop, debug, deploy, deliver and decommission cognition-attached applications. In other words, with the faster adoption and adaption of Microservices architecture (MSA), the faster and simpler realization of process-aware, people-centric, cloud-hosted, event-driven, service-oriented, knowledge-filled, and composite applications is at hand.

The deeper connectivity among all sorts of participating and contributing devices and digitized entities is laying a stimulating and sustainable foundation for the salivating era of big, fast, streaming and IoT data. Similarly, there are a number of processing methods such as batch, real-time, streaming, interactive and iterative processing that are well-supported by competent data processing frameworks. There is a myriad of Hadoop distributions for easing out big data analytics, robust and resilient real-time processing platforms such as Apache Spark, SAP HANA, etc. and versatile streaming analytics solutions such as Kafka and Spark streaming, Flink, Storm, etc. Similarly, there are several product vendors bringing forth IoT application enablement platforms (AEPs) and analytics solutions that are made available on local, traditional IT and cloud environments for wider subscription and usage.
Finally, the era of cognition is slowly yet steadily set in. Every process, infrastructure, service, system, and solution are becoming cognitive with the unprecedented and delectable advancements in the fields of artificial science (AI), natural language processing (NLP), machine learning (ML), neural networks (NN), genetic algorithm (GA), etc. The complicated process of data analytics is also set to become cognitive to produce innumerable sophisticated applications. All kinds of big, discrete, and continuous data are bound to be captured, cleansed, and crunched through a litany of automated analytics tools to generate timely, tactic and strategic insights.

Having understood the strategic implications of the IoT domain for the total humanity, we have recently brought in a comprehensive yet compact book. The book is stuffed with a lot of content explaining the various technologies, techniques, tips and tools. A few chapters are specially crafted for conveying the powerful industry use cases that are being accomplished through the IoT concepts. There is a chapter on edge/fog computing in order to accentuate edge analytics through edge clouds. The IoT security challenges and concerns are manifold and hence there is a chapter on that topic too. The link for the book is http://www.amazon.in/Internet-Things-Enabling-Technologies-Platforms/dp/1498761283.

A New Book on the IoT Technologies and Tools https://www.amazon.com/Internet-Things-Enabling-Technologies-Platforms/dp/1498761283/ref=sr_1_1?ie=UTF8&qid=1488991592&sr=8-1&keywords=the+internet+of+things+book+pethuru+raj This book supplies all the right and relevant details about the various technologies, tools, tips, and techniques of the Internet of Things (IoT). A variety of use cases is inscribed for practitioners as well as research scholars, scientists, and students.

Monitoring Docker

It is an indisputable truth that the paradigm of compartmentalization (virtualization and containerization) is a key contributor in fast-tracking the cloud computing model. With a bevy of industry-strength virtualization technologies and tools, IT infrastructures of worldwide institutions, individuals and innovators are being reinvigorated and refurbished to be extremely programmable, open, workload-aware, and affordable.  Scores of powerful automation tools have precipitated the establishment and sustenance of highly resilient and robust infrastructures (servers, storages, and network solutions) and thereby the business goal of more with less is being met comfortably by IT. In the recent past, the aspect of containerization is being smartly applied in order to bring forth deeper and decisive augmentation, acceleration, and automation on the IT front. The Docker platform is the widely known and overwhelmingly accepted open-source solution for spearheading the containerization era. Due to a few significant advancements being brought in by the containerization movement, slowly yet steadily the Docker platform is being leveraged across not only on development, testing, staging environments but also in IT production environments.  Precisely speaking, Docker is emerging as the new-generation production-ready technology as the Docker ecosystem is consistently on the rise.

Therefore, there are a variety of tools (commercial-grade as well as open source) being built for enabling container communication, clustering, monitoring, metering, management, and maintenance. For virtualized clouds, there are a number of highly synchronized platforms and dashboards for efficient use of virtual resources. Similarly for containerized clouds, there is a clarion call for integrated platforms and solutions to speed up the containerization adoption.

In this book, the author meticulously has surveyed the leading Docker monitoring tools and articulated their uniqueness in minutely monitoring the various parameters of container resources and workloads. This book is a must for every aspiring as well as experienced IT system administrators. As clouds are being positioned as the next-generation IT environment and containerization is sweeping the IT space, every cloud administrator and data center operators need to have a copy of this very practical and nicely written book. The author has just produced a highly useful and usable book from his vast experiences. It is an easy-to-grasp book as it is stuffed and saturated with a litany real-world examples on how to accomplish tools-based Docker monitoring.

By Pethuru Raj

A Master Piece on HBase Design Patterns

The discipline of Big Data Analytics (BDA) is fast gaining a lot of market and mind shares as the realization technologies, techniques and tools innately enabling BDA are stabilizing and maturing in an unprecedented fashion with the overwhelming support from different stakeholders including worldwide product and platform vendors, analytics researchers, open source community members, IT service organizations, cloud service providers (CSPs), etc. HBase is one of the open-source NoSQL database technologies facilitating the simplification and streamlining of the originally complicated BDA. In this book, the authors have brought in a number of pragmatic design patterns and best practices in order to precisely leverage the HBase technology in implementing enterprise-scale, modular and scalable big data applications. The beauty is that the design patterns tightly associated with HBase could be easily used for other NoSQL databases.

The initial chapters cover what HBase is and how it can be installed in a single or multiple computers. Then there is an easy-to-use example of Java code to read and write data in HBase. The book covers the simplest HBase tables to deal with single entities, such as the table of users. Design patterns here emphasize on scalability, performance, and planning for special cases such as restoring forgotten passwords. It covers how to store large files in HBase systems, talks about the alternative ways of storing them and the best practices extracted from solutions for large environments, such as Facebook, Amazon, and Twitter. The book illustrates how stock market, human health monitoring, and system monitoring data are all classified as time series data. The design patterns for this organize time-based measurements in groups, resulting in balanced, high-performing HBase tables. A chapter is specially allocated to discuss one of the most common design patterns for NoSQL de-normalization, where the data is duplicated in more than one table, resulting in huge performance benefits. It shows you how to implement a many-to-many relationship in HBase that deals with transactions using compound keys. The final chapter covers the bulk loading for the initial data load into HBase, profiling HBase applications, benchmarking, and load testing.

This book is a must for Hadoop application developers. The authors, based on their vast experiences and educations, have clearly articulated the principal patterns in order to lessen the workload on software developers. The key differentiator is that the book is stuffed and sandwiched with a lot of examples and useful tips to enable learners to quickly as well as formally understand the nitty-gritty of design patterns in swiftly and sagaciously building and sustaining next-generation HBase applications.

Learning OpenStack High Availability

High availability has been one of the prime non-functional requirements for cloud environments. Steadily mission-critical workloads are being modernized, hosted, and delivered from cloud environments and hence ensuring their availability all the time for guaranteeing business continuity and for substantially enhancing the quality of experience (QoE) is very paramount for boosting the confidence of business executives on the raging cloud idea. And on the other side, clouds are typically shared and used by multiple customers across and hence, there are possibilities for bringing down cloud systems through deliberate attempts, undetected and hidden system errors, misadventure by administrators, or by establishing and enforcing wrong policies. There are both internal and external issues that can lead to system slowdown and even to breakdown.

Thus designing high-availability IT systems is essential for worldwide enterprises to survive in the increasingly competitive market. As OpenStack is the highly synchronized platform for all kinds of clouds, IT experts, architects and developers ought to deftly leverage the intrinsic capabilities of OpenStack platform towards establishing and sustaining highly available clouds. This book has all the right and relevant information (theory as well as practical) for any IT professional to easily grasp the high-availability tips, techniques, and tools to proceed with the implementation with all the clarity and confidence. The author has chipped in a lot of useful and usable information in this book and I would strongly request worldwide cloud architects and consultants to pick a copy of this book to be sufficiently enriched and empowered to bring forth pioneering high-availability designs and architectures.

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