El. knyga: Data Warehousing and Analytics: Fueling the Data Engine

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: “Part I – Star Schema” describes the foundation of data warehouse design. “Part II – Snowflake and Bridge Tables” then expands the concept of a simple star schema by introducing the concept of hierarchy, bridge tables, as well as the use of bridge tables in temporal data warehousing. “Part III – Advanced Dimensions” elaborates various dimension models, namely determinant dimensions, junk dimensions, dimension keys, and one-attribute dimensions, which all enrich the semantics of the star schema. “Part IV – Multi-Fact and Multi-Input” introduces multi-fact star schemas, where the star schema has multi-fact entities. A multi-fact can also be created by slicing one fact into multi-facts, which is discussed next. Eventually the creation of a star schema is introduced where an operational database is used as input to the transformation process and consists of multiple operational databases. “Part V – Data Warehousing Granularity and Evolution” first introduces the concept of aggregation levels in a star schema constellation. It then focuses on the lowest-level star schema, including how to design a star schema and why it is needed in data warehousing, before moving on to methods for adding and removing dimensions. The remaining two chapters present more advanced concepts in data warehousing granularity and introduce the concept of active data warehousing. “Part VI – OLAP, Business Intelligence, and Data Analytics” thoroughly explains OLAP – online analytical processing, and describes two important activities in the data warehousing process, namely pre-data warehousing and post-data warehousing. The final chapter focuses on data analytics, which consists of a suite of methods for data analysis suitable for data warehousing.

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.