Wenny Rahayu – författare

The growth in grid databases, coupled with the utility of parallel query processing, presents an important opportunity to understand and utilize high-performance parallel database processing within a major database management system (DBMS). This important new book provides readers with a fundamental understanding of parallelism in data-intensive applications, and demonstrates how to develop faster capabilities to support them. It presents a balanced treatment of the theoretical and practical aspects of high-performance databases to demonstrate how parallel query is executed in a DBMS, including concepts, algorithms, analytical models, and grid transactions.

High-Performance Parallel Database Processing and Grid Databases serves as a valuable resource for researchers working in parallel databases and for practitioners interested in building a high-performance database. It is also a much-needed, self-contained textbook for database courses at the advanced undergraduate and graduate levels.

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.