docs/description.md

RubikBench

Database Description

RubikBench is a database containing the financial data of APEX, an (imaginary) international automobile manufacturing and sales company. As a financial database, it is designed to support various analytical queries related to the company's operations, sales, and financial performance. This (imaginary) company operates mainly in China, the United States, and Europe. Therefore, the database is bilingual, with both English and Chinese values, and uses three currencies: CNY, USD, and EUR (the values are interchangeable rather than cumulative; EUR and USD figures are derived from CNY based on the world monthly exchange rates).

There are 6 key dimensions: Period (time, monthly), Product, Region, Customer, Dealer, and Report Item (revenues and expenses). Extra dimensions include Contract, Project, Currency, and Caliber.

The minimal granularity of the data is a single project payment. Each project occurs between APEX and a customer in a specific region, with an optional dealer, over a specified period of time. Payments for a project can be distributed over multiple months within that time period. Each project may contain multiple products. Each contract may contain multiple projects.

RubikBench contains 20 tables in 4 major categories. Each is an aggregated view over the fact table, which is not exposed directly:

• The INCOME tables, which contain data aggregated over project and dealer, and only include revenues. The INCOME tables are the smallest tables in RubikBench, aiming for quick analytical queries.
• The BUDGET_AND_FORECAST tables, which contain data aggregated over project, customer, and dealer. These tables contain only forecast/budget/target values, not actual revenues or expenses. Notice that the semantics of these values could be counter-intuitive: While the target value is the target for monthly revenues and expenses in YYYYMM, as one would expect; the forecast value of YYYYMM is the forecast of YYYY12 (yearly) based on the information available at the end of YYYYMM; the budget of YYYYMM is a constant value indicating the yearly budget duplicated for each month in the year.
• The PROFIT_AND_LOSS table, which contains data aggregated only over the project. It contains the most comprehensive dimensions and measures, including both revenues and expenses. It is the largest table in RubikBench, aiming to support detailed financial analysis.
• The SALES_LEDGER table, which contains the lowest granularity data, i.e., payment-level data. It is designed to support detailed audit and traceability of revenues. However, it is limited to sales revenues and expenses only.

The products of APEX are divided into 3 major divisions: Automobiles, Accessories, and Services.

• Automobiles are produced by enterprise brand groups, which are 6 sub-brands under APEX. Each brand group has its own INCOME and BUDGET_AND_FORECAST tables.
• Accessories and Services each have their own INCOME and BUDGET_AND_FORECAST tables. Accessories only have equipment revenues and costs, while services only have service revenues and costs.

The default Caliber is code A, which clearly separates equipment and service report items, reflecting the real financial statistics. However, to facilitate the cooperation between different divisions, there is also Caliber B, which moves 5% of service revenue to equipment revenue.

Notice that due to historical reasons as well as query speed expectations, currencies and calibers are organized differently across different tables. For example, for the INCOME and PROFIT_AND_LOSS tables, different currencies and calibers are stored in different columns as column name suffixes (e.g., _cny_a, _usd_a, _eur_b, etc.); while for the SALES_LEDGER and BUDGET_AND_FORECAST tables, caliber and currency are separate columns that MUST be filtered on in the query predicates to avoid duplicated results.

Financial amounts present both ptd (monthly) values and ytd (year-to-date) values. For example, ptd of YYYYMM means the amount for that month, while ytd of YYYYMM means the cumulative amount from YYYY01 to YYYYMM (inclusive). Furthermore, _py columns contain previous year data, which means that, for example, ytd py columns with period='YYYYMM' contain cumulative amounts from YYYY-1 01 to YYYY-1 MM, etc.