How to Build Analytics Data Models With dbt for Marketing and Product Teams
dbt transforms raw analytics data into clean, reliable datasets. Here's the starter guide for marketing and product analytics models.
Raw analytics data is messy. Events fire multiple times, user IDs change, timestamps need timezone adjustments, and joining data from different sources requires careful logic. dbt (data build tool) handles these transformations in a version-controlled, testable workflow.
The starter models for marketing and product analytics include: a unified user model (stitching identities across tools), a session model (grouping events into sessions with attribution), a funnel model (tracking conversion through defined steps), and a revenue model (connecting user behavior to payment data).
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We cover the dbt project setup for analytics, the SQL patterns for each model, the testing framework that validates data quality, and the scheduling setup that refreshes models daily. This guide assumes you have a data warehouse (BigQuery, Snowflake, or Redshift) and basic SQL knowledge.
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