How to Design a Data Warehouse Architecture for Marketing and Product Analytics
A well-designed warehouse architecture unifies marketing, product, and revenue data. Here's the reference architecture for SaaS companies.
Most SaaS companies outgrow tool-native analytics and need a data warehouse to combine marketing, product, and revenue data into unified models. The architecture decisions you make at this stage determine whether the warehouse becomes an asset or a burden.
The reference architecture has four layers: ingestion (Fivetran or Airbyte pulling data from all sources), storage (BigQuery or Snowflake as the warehouse), transformation (dbt for cleaning and modeling data), and visualization (Looker, Metabase, or Preset for dashboards and exploration).
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We cover the vendor selection criteria for each layer, the data source prioritization (start with CRM, then analytics, then product data), the core data models to build first (unified customer, attribution, funnel), and the team structure needed to maintain and extend the warehouse over time.
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