Calling chapters for the planned book on the topic “The Cloud and Fog Computing Infrastructures for Data Science”
Thanks and regards
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
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.
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.