DataWorks Data Modeling follows the dimensional modeling methodology developed by Ralph Kimball. When you use the dimensional modeling feature of DataWorks to design a data warehouse model, you can design and create dimension tables, fact tables, and aggregate tables based on your business requirements, and quickly publish the model to an R&D engine. You can use the reverse modeling feature to apply the models generated by using other modeling tools to DataWorks Dimensional Modeling.

Dimension table

Extract all the dimensions that possibly exist in each data domain, and store the dimensions and attributes of the dimensions in dimension tables. For example, when you analyze e-commerce business data, possible dimensions (attributes of each dimension) include order (order ID, order creation time, buyer ID, and seller ID), user (gender and birthdate), and commodity (commodity ID, commodity name, and commodity put-on-shelf time). In this case, you can create the following dimension tables: order dimension table, user dimension table, and commodity dimension table. The attributes of each dimension are used as the fields in the dimension table. You can deploy the dimension tables in a data warehouse and perform extract, transform, load (ETL) operations to store dimension data in the format defined in the dimension table. This allows business personnel to access the data for subsequent data analysis.

For more information about how to design and create a dimension table, see Create a dimension table.

Fact table

Sort and analyze data that is generated in each business process, and store the data in fact tables as fields. For example, you can create a fact table for the business process of placing an order, and record the following information as fields in the fact table: order ID, order creation time, commodity ID, number of commodities, and sales amount. You can deploy the fact tables in a data warehouse and perform ETL operations to summarize and store data in the format defined in the fact table. This allows business personnel to access the data for subsequent data analysis.

For more information about how to design and create a fact table, see Create a fact table.

Aggregate table

Summarize and analyze fact data and dimension data based on business data analysis and data layering, and create an aggregate table. This way, you can directly access data in the aggregate table for subsequent data analysis without the need to access the data in fact tables and dimension tables.

For more information about how to design and create an aggregate table, see Create an aggregate table.

Reverse modeling

Reverse modeling is used to apply the models generated by using other modeling tools to DataWorks Dimensional Modeling. For example, if you generated a model by using other modeling tools and you want to use DataWorks Data Modeling for subsequent modeling, you can use the reverse modeling feature of DataWorks. This feature eliminates the need for second modeling. It helps you quickly apply existing models to DataWorks Dimensional Modeling, and therefore saves you a lot of time.

For more information about reverse modeling, see Reverse modeling.