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Dimensional modeling

As explained in First-Party Data Activation

A data modeling technique that organizes analytical data into facts and descriptive dimensions for reporting and querying.

Dimensional modeling organizes analytical data for practical reporting and cohort targeting, especially in the lakehouse’s silver or gold layers. Its star and snowflake schemas separate query-friendly analysis from descriptive structure. section 5.4.2

Choose the form according to analytical priorities:

  • Prefer star schemas when dashboards and straightforward querying matter most. section 5.4.2
  • Use snowflake schemas when reducing redundancy justifies additional joins. section 5.4.2
  • Validate the model against campaign questions such as targeting lapsed buyers or segmenting email-click behavior. section 5.4.2

How First-Party Data Activation covers it

How deep it goes
Contrasts star and snowflake schemas by query simplicity and redundancy, but does not detail implementation or governance mechanics.
Examples
Uses dashboards, lapsed-buyer targeting, and email-click segmentation as concrete modeling requirements.
What you can do after
Evaluate star and snowflake schema choices against reporting, querying, redundancy, and campaign-targeting needs.
How it connects
Connects dimensional modeling with ER modeling, data vault modeling, lakehouses, dashboards, segmentation, and campaign analysis.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of First-Party Data Activation. The section links point to the parts of the book it draws on.