This section provides background information on data modeling, including some best practices associated with building your models in CloudConnect LDM Modeler. A component of the CloudConnect Designer, the CloudConnect LDM Modeler enables the project developer to quickly build and test the logical data model for a project and, when ready, to immediately 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) is a representation of the relationships 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 in order to define data relationships within the project.
- In GoodData, a datamart corresponds to a GoodData project.
- The logical data model is the contract between the data loading process and the datamart and between the datamart and the analytical queries.
- It maps the incoming data to the physical data model, which is used to store the content in the data warehouse.
The logical data model provides a layer of abstraction so that users do not need to interact with the physical data model.
The logical data model enables a layer of abstraction between the information the GoodData user is accessing and the method by which the data is stored. 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 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.
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