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    Dataphin:Data standardization and modeling

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    Dataphin:Data standardization and modeling

    Last Updated:Jun 03, 2026

    Dataphin follows Ralph Kimball's dimensional modeling theory. You design conceptual models based on your business, then create dimension tables, fact tables, atomic metrics, business filters, metrics, and logical aggregate tables from the model's business entities (business objects and business activities).

    Prerequisites

    • The intelligent R&D module is activated for your tenant. Activate it in Tenant Management.

    • The project is bound to a data section. Projects with incorrect or missing bindings cannot use data modeling. Bind a project in Create a General Project.

    • The default feature menu items selected during project creation include data modeling modules. Configure them in Create a General Project.

      Note

      Basic projects linked to Basic Mode data sections support data modeling. Basic projects linked to Dev-Prod Mode data sections only support data processing and ad hoc queries.

    Features

    The following table describes each modeling feature:

    Feature item

    Description

    References

    Logical dimension table

    Logical dimension tables store attribute details of business objects. Create tables matching each business object type.

    Logical fact table

    Logical fact tables store data generated by business activities. Create tables matching each business activity type.

    Create and configure a logical fact table.

    Atomic metric

    Atomic metrics define the statistical scope and calculation logic for a business measure. Standardized metric definitions ensure consistent results and improve R&D efficiency. Example: payment amount.

    Create atomic metrics.

    Composite metric

    Composite metrics derive calculations from multiple atomic metrics. Example: C=A/B.

    Create a composite metric.

    Derived metric

    Combines an atomic metric with business filters, a statistical period, and dimensions to produce a business statistic. Derived metric = Atomic metric + Business filter + Statistical period + Dimension(s) (statistic granularity).

    Create derived metrics.

    Business filter

    Business filters filter records by business rules, similar to SQL WHERE conditions (excluding time intervals). While atomic metrics define calculation logic, business filters define condition constraints.

    Create business filters.

    Logical aggregate table

    Logical aggregate tables group derived metrics that share the same statistic granularity and period. Each derived metric is associated with a single logical aggregate table. The primary key consists of dimensions from logical dimension tables (the statistic granularity); all non-key fields are metrics.

    Access data modeling

    1. On the Dataphin home page, click R&D in the top menu bar.

    2. The left navigation pane of the Data Development page lists entry points for each data modeling module.

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