The Unified Star Schema: An Agile and Resilient Approach to Data Warehouse and Analytics Design

by Bill Inmon, Francesco Puppini

Master the most agile and resilient design for building analytics the Unified Star Schema (USS) approach. The USS has many benefits over traditional dimensional modeling. Witness the power of the USS as a single star schema that serves as a foundation for all present and future business requirements of your organization. Data warehouse legend Bill Inmon and data warehouse expert, Francesco Puppini, explain step-by-step, why the USS approach is the preferred approach for business intelligence designs today, and how to use this approach through many examples. This book contains two parts. Part I, Architecture, explains the benefits of data marts and data warehouses, covering how organizations progressed to their current state of analytics, and to the challenges that result from current business intelligence architectures. Chapter 1 covers the drivers behind and the characteristics of the data warehouse and data mart. Chapter 2 introduces dimensional modeling concepts, including fact tables, dimensions, star joins, and snowflakes. Chapter 3 recalls the evolution of the data mart. Chapter 4 explains Extract, Transform, and Load (ETL), and the value ETL brings to reporting. Chapter 5 explores the Integrated Data Mart Approach, and Chapter 6 explains how to monitor this environment. Chapter 7 describes the different types of metadata within the data warehouse environment. Chapter 8 progresses through the evolution to our current modern data warehouse environment. Part II, the Unified Star Schema, covers the Unified Star Schema (USS) approach and how it solves the challenges introduced in Part I. There are eight chapters within Part Chapter 9, Introduction to the Unified Star Learn about its architecture and use cases, as well as how the USS approach differs from the traditional approach. Chapter 10, Loss of Learn about the loss of data and the USS Bridge. Understand that the USS approach does not create any join, and for this reason, it has no loss of data. Chapter 11, The Fan Get introduced to the Oriented Data Model convention, and learn the dangers of a fan trap through an example. Differentiate join and association, and realize that an "in-memory association" is the preferred solution to the fan trap. Chapter 12, The Chasm Become familiar with the Cartesian product, and then follow along with an example based on LinkedIn, which illustrates that a chasm trap produces unwanted duplicates. See that the USS Bridge is based on a union, which does not create any duplicates. Chapter 13, Multi-Fact Distinguish between multiple facts "with direct connection" versus multiple facts "with no direct connection". Explore how BI tools are capable of building aggregated virtual rows. Chapter 14, Learn more about loops and five traditional techniques to solve them. Follow along with an implementation, which will illustrate the solution based on the USS approach. Chapter 15, Non-Conformed Learn about non-conformed granularities, and learn that the Unified Star Schema introduces a solution called "re-normalization". Chapter 16, Northwind Case Study. Witness how easy it is to detect the pitfalls of Northwind using the ODM convention. Follow along with an implementation of the USS approach on the Northwind database with various BI tools.

296 pages, Kindle Edition

Published October 4, 2020

Reviews

[Laurentiu Diaconu · Rating 3 out of 5]

I have to say this book was sort of underwhelming. It basically recommends going the USS way to resolve issues that are caused by either:
- BI tooling not knowing how to perform fact drill-across (multi-fact queries) and filter/context transition or
- the above, but applied to users reading data from a dimensional model

I can understand the rationale of presenting this option for BI tooling that just does not do it properly, but that likely speaks more about whether the said BI tooling should be used in the first place, not of the pitfalls of dimensional modeling? The bits presented by the book as typical issues, especially from a SQL perspective, are all side-stepped by the developer following best practices in the first place. It's hard to say that the developer not following best practices when querying data is the fault of the underlying modeling practices.

Plus, the book makes the case that any business user would ever consume data in a detailed report that is the result of an union from multiple business processes, looking sparse and all. This is very considerable departing from the usual reality of business users consuming data.

I think this is still a 3/5 considering it outlines typical issues faced by people using dimensional models that are beyond a simple star. But since it does not explain how to properly solve these and think about them without going the "switchboard" way, I would say you should rather read "Star Schema - The Complete Reference" by Christopher Adamson (https://www.goodreads.com/en/book/sho...) for a proper take on this.

[Ohad Srur · Rating 5 out of 5]

A must read for any data practitioner who need to model data

The author describes very well the existing challenges with Kimball and how his USS approach can solve it. I highly recommend this book to anyone who plan to build a data warehouse.

[Rajib Deb · Rating 2 out of 5]

I did not find this book much useful.

The examples did not look practical. It would have been good if the examples reflected a real and a more complex usecase.

[Bruce]

See notes in reader/obsidian