Numbers that you want to use to slice your data (for example, table size) are stored as attributes. Numbers that you want to aggregate by sum, count, minimum, maximum, or average must be stored as facts. The model is based on the datasets that are used in the GoodData Demo Workspace.ДЄll text values are stored as attributes.ДЇact in a dataset OR attribute in a dataset The following image represents a simple LDM visualization in the LDM Modeler. When properly constructed, the LDM defines the datasets and the connections between them and delivers the power to calculate predefined metrics and reports without forcing the platform to do complicated joins or lookups. You can think of datasets as of virtual tables.Д®ach workspace requires an LDM. In GoodData, these logical sets are called datasets. The LDM describes the relationships between abstracted data elements, sets of data that are organized by logical connection rather than associated by how and where they are stored. Logical Data Model and Your GoodData Workspace When a data loading process is executed, the GoodData platform references the LDM to determine how the incoming data is written into the designated dataset.ДЄny request for data submitted from a GoodData interface is passed through the LDM to retrieve the data and to return it to the querying client. In the GoodData platform, the LDM is used to perform the following basic functions: This layer of abstraction allows continuous improvement of the physical data model and the tools used to access and maintain it without interfering with the userвЂ™s definition of the data architecture. The LDM enables a layer of abstraction between the information the GoodData user is accessing and the method by which the data is stored. The LDM provides a layer of abstraction so that users do not need to interact with the physical data model. Your LDM maps the incoming data to the physical data model, which is used to store the content in the data warehouse. You can see the LDM as the contract between the data loading process and the data warehouse, and between the data warehouse and the analytical queries. This level of abstraction enables greater focus on the data relationships and simpler management of the architecture of the workspace. On the GoodData platform, data modelers never need to create or directly interact with the physical data model.

Data modeling using dbschema