Data warehouse planning is a critical initial step in establishing a data mid-end with Dataphin, serving as a foundational component of the data architecture's top-level design. Prior to embarking on data development, it is essential to outline the data warehouse plan, which encompasses the definition of data blocks, projects, data sources, compute sources, and statistical periods.
Feature introduction
Data warehouse planning involves the creation of a global schema within logical and physical spaces to facilitate business segmentation, resource management, and project oversight:
Logical space: Segregates namespaces and their core objects according to business characteristics, which include data blocks, data domains, and modules for common business logic.
NoteNote: The Basic R&D Edition of the Dataphin instance does not support data blocks, data domains, and common business logic modules.
Physical space: Organizes physical projects to meet the needs of collaborative development management, which encompasses project and data source management, among other modules.
The Basic R&D Edition and the Intelligent R&D Edition offer distinct features:
Basic R&D Edition: Planning encompasses two principal modules: Project and Source. Dependencies exist between these modules, and the project's compute and storage resources are configured based on Compute Source, while the project space utilizes data based on Datasource.
Intelligent R&D Edition: Planning includes the modules Business, Project, and Source. These modules are interdependent, and the project's compute and storage resources are based on Compute Source, while the project space uses data from Datasource. Additionally, projects can be associated with data blocks.
Data warehouse planning entry
On the Dataphin home page, single click the Planning button in the top menu bar.
In the data warehouse planning page, the left-side navigation pane provides access to each feature module's entry buttons.
