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This section provides background information on data modeling, including on best practices to build your models in CloudConnect LDM Modeler.

CloudConnect LDM Modeler is a component of the CloudConnect Designer and it enables project developers to quickly build and test the logical data model for a project and, when ready, to deploy it to the corresponding GoodData project on the platform.

CloudConnect Designer is a desktop Java-based application that you use to design and implement ETL processes for your GoodData projects. Download and install this application on your local desktop. For more information on downloading and installing CloudConnect Designer, see the Downloads page.

A logical data model (LDM) represents the relationship between data objects in a datamart. In the LDM Modeler, you assemble datasets and other objects, customize them, and build the connections between these objects to define data relationships within the project.

  • In GoodData, a datamart corresponds to a GoodData project.
  • A logical data model is the contract between the data loading process and the datamart and between the datamart and the analytical queries.
    • LDM maps the incoming data to the physical data model, which is used to store the content in the data warehouse.
    • LDM provides a layer of abstraction between the information the GoodData user is accessing and the method by which the data is stored so that users do not need to interact with the physical data model.

      This layer of abstraction allows for continuous improvement of the physical data model and the tools used to access and maintain it without interfering with the user's definition of the data architecture.

  • Built in CloudConnect Designer, the ETL processes are composed of graphs and some metadata. Dimensional data models are optimized for querying. After the data is loaded into the datamart, almost all user operations involve querying the datamart to retrieve results for display in a report. Dimensional data models are also fairly easy to extend to accommodate new types of data.

For detailed information about working with data modeling, see the following sections: