Data Warehousing and Analytics: Fueling the Data EngineКНИГИ » ОС И БД
Название: Data Warehousing and Analytics: Fueling the Data Engine Автор: David Taniar, Wenny Rahayu Издательство: Springer Год: 2022 Страниц: 642 Язык: английский Формат: pdf (true) Размер: 42.5 MB
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.
Through all these years, I have come to regard data as a “many-splendored” thing for two primary reasons. One is that data has to be looked at from many different perspectives in order to understand its meaning. In this regard, statistics, databases, and AI have pursued the same problem. Another is that managing data entails many difficult practical issues. They include data modeling, the ever-expanding variety of data sources, many data types, data quality, Big Data, security and privacy, migration, visualization, etc.
From these perspectives, there are three reasons I believe this book Data Warehousing and Analytics – Fueling the Data Engine is a noteworthy resource for students and professionals working in the field of data management. First, this is a comprehensive book on data warehousing. It takes the readers on an “end-to-end” journey on all data warehouse modeling concepts and techniques to create and use (i.e., query) data warehouses. In other words, the book deals with many of the difficult issues in understanding the meaning of data and using the data by modeling complex data warehouses, creating (i.e., populating the data warehouses), and extracting the desired information (by issuing SQL queries against the data warehouses).
To be sure, these days people gravitate towards topics such as driverless cars, humanoids, Deep Learning, chatbots, Machine Learning analytics, and the like rather than data warehousing, which has a long history. However, the criticality of data warehouses as a business resource has only increased over the years. Furthermore, the new-fangled technologies are, in a sense, data engines fueled by data, including data systematically cleaned and stored and updated in data warehouses.
Second, although this is a comprehensive book, I think it should be easy to read, requiring only an ordinary effort from readers with a reasonable knowledge of relational databases. In other words, the “end-to-end” journey should be a pleasant one for the readers. The authors start with a simple star schema, then bring in hierarchies and bridge tables, then delve into determinant dimensions and multi-fact schemas. They build up the most complex data warehouse incrementally in a methodical and disciplined way.
Third, although in each chapter, the authors introduce each data warehouse modeling concept and its implementation in an easy-to-understand way, they include case studies and exercises at the end of each chapter. I believe this approach helps the readers really understand the materials and prepare them to apply what they have learned.
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Data Warehousing and Analytics: Fueling the Data Engine 
