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Organization of data warehouses are vital but often ignored aspects of growing enterprises. This work merges technological know-how with managerial practices to show both the business manager and the IT professional how better alignment between data warehouse plans and business strategies can lead to a successful data warehouse adoption that will support the entire infrastructure. More complete than any other text in the field, this resource also addresses the managerial and strategic aspects of data warehouses, offering doable solutions that will allow for the strategic alignment of these warehouses while building them and ensuring that this alignment is sustained.
What is the key to a successful data warehouse? Strategy, design, implementation! This book skillfully provides a conceptual and working model of a successful data warehouse process that is developed by using SAS software. Written for both the business and technical sides of the house, Welbrock supplies real-life experience with data warehousing, not a theoretical approach. This book delivers the following three-phase strategy for building a data warehouse: 1) Build a conceptual data warehouse that contains metadata about the business elements that are documented in the conceptual warehouse into a technological lexicon. 3) Implement the physical data warehouse. Topics discussed that support the data warehousing process are data modeling, data transformation, multi-dimensional databases, data extraction and storage, warehouse loading, client/server, and SAS/Warehouse Administrator. The Strategy for designing your effective data warehouse is found in these pages!
Dyche presents the complete manager's briefing on what data warehousing technology can do today and how to achieve optimal results. Using real-world case studies from Charles Schwab, Bank of America, Qantas, 20th Century Fox, and others, she covers decision support, database marketing, and many industry-specific data warehouse applications.
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Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. - Learn how to leverage Big Data by effectively integrating it into your data warehouse. - Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies - Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements
Geared to IT professionals eager to get into the all-importantfield of data warehousing, this book explores all topics needed bythose who design and implement data warehouses. Readers will learnabout planning requirements, architecture, infrastructure, datapreparation, information delivery, implementation, and maintenance.They'll also find a wealth of industry examples garnered from theauthor's 25 years of experience in designing and implementingdatabases and data warehouse applications for majorcorporations. Market: IT Professionals, Consultants.
Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization.
In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Advanced Data Warehousing Strategies: Building Scalable and High-Performance Data Storage Solutions, is conceived to bridge the gap between emerging technological advancements in data warehousing and their strategic application in building efficient, scalable, and high-performance data storage systems. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of data warehousing technologies, particularly focusing on techniques and applications relevant to modern data storage solutions. From foundational theories to advanced applications, we delve into the critical aspects that drive successful innovation in large-scale data systems. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, developers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world data warehousing scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from data warehousing fundamentals and optimization techniques to the strategic management of scalable storage systems. Additionally, we emphasize the importance of high performance and data integrity, dedicating sections to the art of developing data solutions that deliver efficiency, scalability, and resilience. The inspiration for this book arises from a recognition of the crucial role that data warehousing systems play in shaping the future of digital interactions and business intelligence. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how data warehousing strategies can be harnessed to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating high-performance data storage solutions that will define the future of enterprise data management. Thank you for joining us on this journey. Authors
Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: - Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. - Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. - Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way. - Learn how to quickly define scope and architecture before programming starts - Includes techniques of process and data engineering that enable iterative and incremental delivery - Demonstrates how to plan and execute quality assurance plans and includes a guide to continuous integration and automated regression testing - Presents program management strategies for coordinating multiple agile data mart projects so that over time an enterprise data warehouse emerges - Use the provided 120-day road map to establish a robust, agile data warehousing program
Data exists in abundance but it is often unusable to support decision making because it is unstructured, unintegrated, aged or polluted. Data warehousing explores the issue whether to build a full enterprise data warehouse, or whether to go for a scaled down "data mart" It is updated to include the latest developments, acronyms, and techniques, and compares and contrasts relational and multidimensional databases It also evaluates the use of data warehousing to support operational processing reports on innovative designs for optimal performance of relational databases for a "query intensive" world analyzes artificial intelligence data mining tools.