Overview

Model Design Method Overview

A data model can reflect the relationships between objects. It incorporates the key information features extracted based on business requirements. It visually represents how the internal information of an enterprise is organized. A data model must be capable of simulating scenarios, easy-to-understand, and easily implemented in the IT system.

DataArts Architecture provides the following modeling methods:

• ER modeling

  ER modeling describes the business processes within an enterprise. Compliant with the third normal form (3NF), ER modeling is designed for data integration. It is used for combining and merging data with similarities by subject. ER modeling results cannot be used directly for decision-making, but they are a useful tool.

  During ER modeling, you can design physical models in data warehouse planning.

  • Physical model: An advanced version of the logic model and used to design the database architecture for data storage with a full consideration of various technical factors. For example, the selected data warehouse is DWS or MRS_Hive.
• Dimensional modeling

  Dimensional modeling is the construction of models based on analysis and decision-making requirements. It is mainly used for data analysis. Dimensional modeling is focused on how to quickly analyze user requirements and respond rapidly to complicated, large-scale queries.

  A multidimensional model is a fact table consisting of numeric metrics. The fact table is associated with a group of dimensional tables containing description attributes with primary or foreign keys. Typical dimensional models include star models and snowflake models used in some special scenarios.

• Data mart

  A data mart (DM) aggregates data from multiple layers and consists of a specific analysis object and its related metrics. The DM provides all statistical data by subject.

In the DataArts Architecture module of DataArts Studio, dimensional modeling involves abstracting facts and dimensions for model creation, and abstracting and sorting out report requirements for constructing metric systems and creating summary models using the data mart.

DataArts Architecture Overview Page

On the DataArts Studio console, locate a workspace and click DataArts Architecture. The Overview page is displayed.

Figure 1 DataArts Architecture Overview page

• My To-Dos
  • The My To-Dos area displays the quantity of My Applications and Pending Review.
  • Click the numbers above My Applications and Pending Review to access the My Applications and Pending Review pages, respectively.
• Assets
  • The Assets area displays all the objects in DataArts Architecture.
  • Click the number next to each object name to access the object management page.
• Quick Start

  The Quick Start area displays the overall process for data governance. You can click a specific operation under the process to go to the corresponding page.

• DataArts Architecture Process
  • This area displays the DataArts Architecture process and how the DataArts Architecture module interacts with other modules of DataArts Studio. For details about the DataArts Architecture process, see DataArts Architecture Use Process.
  • You can move the cursor over the name of an object to view its description.
  • You can click the name of any object supported by DataArts Studio to access the object management page.

Information Architecture of DataArts Architecture

An information architecture is a set of component specifications that describe various types of information required for business operations and management decision-making as well as the relationships of business entities. On the Information Architecture page, you can view and manage all tables, including logical entities, physical tables, dimension tables, fact tables, and summary tables.

On the DataArts Studio console, locate a workspace and click DataArts Architecture. In the navigation pane, choose Information Architecture.