With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including conceptual and logical data warehouse design, as well as querying using MDX, DAX and SQL/OLAP. This part also covers data analytics using Power BI and Analysis Services. Part II details “Implementation and Deployment,” including physical design, ETL and data warehouse design methodologies. Part III covers “Advanced Topics” and it is almost completely new in this second edition. This part includes chapters with an in-depth coverage of temporal, spatial, and mobility data warehousing. Graph data warehouses are also covered in detail using Neo4j. The last chapter extensively studies big data management and the usage of Hadoop, Spark, distributed, in-memory, columnar, NoSQL and NewSQL database systems, and data lakes in the context of analytical data processing. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Power BI. All chapters have been revised and updated to the latest versions of the software tools used. KPIs and Dashboards are now also developed using DAX and Power BI, and the chapter on ETL has been expanded with the implementation of ETL processes in PostgreSQL. Review questions and exercises complement each chapter to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available online and includes electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style. “I can only invite you to dive into the contents of the book, feeling certain that once you have completed its reading (or maybe, targeted parts of it), you will join me in expressing our gratitude to Alejandro and Esteban, for providing such a comprehensive textbook for the field of data warehousing in the first place, and for keeping it up to date with the recent developments, in this current second edition.” From the foreword by Panos Vassiliadis, University of Ioannina, Greece.

Decision Support and Data Warehouse Systems ties the more traditional view of decision support to the rapidly evolving topics of database management and data warehouse. As organizations move quickly into networked-based environments, the nature of decision support tools has become increasingly complex. These tools are now used collaboratively and the use of data warehousing mechanisms will be a critical success factor for the survival of many organizations. This new book provides a strong foundation for the use of models within the context of building and using decision support systems, and it will focus on multi-dimensional databases and client/server computing.

You have to make sense of enormous amounts of data, and while the notion of "agile data warehousing might sound tricky, it can yield as much as a 3-to-1 speed advantage while cutting project costs in half. Bring this highly effective technique to your organization with the wisdom of agile data warehousing expert Ralph Hughes. Agile Data Warehousing Project Management will give you a thorough introduction to the method as you would practice it in the project room to build a serious "data mart. Regardless of where you are today, this step-by-step implementation guide will prepare you to join or even lead a team in visualizing, building, and validating a single component to an enterprise data warehouse. - Provides a thorough grounding on the mechanics of Scrum as well as practical advice on keeping your team on track - Includes strategies for getting accurate and actionable requirements from a team's business partner - Revolutionary estimating techniques that make forecasting labor far more understandable and accurate - Demonstrates a blends of Agile methods to simplify team management and synchronize inputs across IT specialties - Enables you and your teams to start simple and progress steadily to world-class performance levels

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.

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This exceptional work provides readers with an introduction to the state-of-the-art research on data warehouse design, with many references to more detailed sources. It offers a clear and a concise presentation of the major concepts and results in the subject area. Malinowski and Zimányi explain conventional data warehouse design in detail, and additionally address two innovative domains recently introduced to extend the capabilities of data warehouse systems: namely, the management of spatial and temporal information.

This textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge. The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics). This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.

For undergraduate/graduate-level Data Mining or Data Warehousing courses in Information Systems or Operations Management Departments electives. Taking a multidisciplinary user/manager approach, this text looks at data warehousing technologies necessary to support the business processes of the twenty-first century. Using a balanced professional and conversational approach, it explores the basic concepts of data mining, warehousing, and visualization with an emphasis on both technical and managerial issues and the implication of these modern emerging technologies on those issues. Data mining and visualization exercises using an included fully-enabled, but time-limited version of Megaputer's PolyAnalyst and TextAnalyst data mining and visualization software give students hands-on experience with real-world applications.