Building Big
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Building Big Business in Russia
This book examines the development of big business in Russia since the onset of market oriented reform in the early 1990s. It explains how privatized post-Soviet enterprises, many of which made little sense as business units, were transformed into functional firms able to operate in the environment of a market economy. It provides detailed case studies of three key companies – Yukos Oil Company, Siberian (Russian) Aluminium and Norilsk Nickel – all of which played a key role in Russia’s economic recovery after 1998, describing how these companies were created, run and have developed. It shows how Russian businesses during the 1990s routinely relied on practices not entirely compatible with formal rules, in particular in the area of corporate governance. The book fully explores the critical role played by informal corporate governance practices - such as share dilution, transfer pricing, asset stripping, limiting shareholders access to votes, and 'bankruptcy to order’ - as Russian big business developed during the 1990s. Unlike other studies on Russian corporate governance, this book highlights the ambiguous impact of informal corporate governance practices on the companies involved as commercial entities, and suggests that although their use proved costly to Russia’s business reputation, they helped core groups of owners/managers at the time to establish coherent business firms. Overall, the book shows that we cannot understand the nature of current economic changes in Russia without recognising the crucial role played by informal corporate governance practices in the creation and development of big business in post-Soviet Russia.
Building Big Data Applications
Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.). - Explores various ways to leverage Big Data by effectively integrating it into the data warehouse - Includes real-world case studies which clearly demonstrate Big Data technologies - Provides insights on how to optimize current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements
Building Big Data and Analytics Solutions in the Cloud
Big data is currently one of the most critical emerging technologies. Organizations around the world are looking to exploit the explosive growth of data to unlock previously hidden insights in the hope of creating new revenue streams, gaining operational efficiencies, and obtaining greater understanding of customer needs. It is important to think of big data and analytics together. Big data is the term used to describe the recent explosion of different types of data from disparate sources. Analytics is about examining data to derive interesting and relevant trends and patterns, which can be used to inform decisions, optimize processes, and even drive new business models. With today's deluge of data comes the problems of processing that data, obtaining the correct skills to manage and analyze that data, and establishing rules to govern the data's use and distribution. The big data technology stack is ever growing and sometimes confusing, even more so when we add the complexities of setting up big data environments with large up-front investments. Cloud computing seems to be a perfect vehicle for hosting big data workloads. However, working on big data in the cloud brings its own challenge of reconciling two contradictory design principles. Cloud computing is based on the concepts of consolidation and resource pooling, but big data systems (such as Hadoop) are built on the shared nothing principle, where each node is independent and self-sufficient. A solution architecture that can allow these mutually exclusive principles to coexist is required to truly exploit the elasticity and ease-of-use of cloud computing for big data environments. This IBM® RedpaperTM publication is aimed at chief architects, line-of-business executives, and CIOs to provide an understanding of the cloud-related challenges they face and give prescriptive guidance for how to realize the benefits of big data solutions quickly and cost-effectively.