Wavelength

Marketing attribution data pipeline diagram

This is a worked example. Wavelength built the diagram below from the description on this page. Nobody drew it by hand.

What was said

Website visits are tracked in Google Analytics 4. GA4 exports raw events to BigQuery every day. Ad spend from Google Ads and LinkedIn Ads also lands in BigQuery through Supermetrics. dbt joins the events and the spend into an attribution model. The marketing team reads cost per pipeline in Looker.

The diagram

G n1 Website Visits n2 Google Analytics 4 n1->n2 tracked in n3 BigQuery n2->n3 exports raw events daily n7 dbt n3->n7 joins events and spend n4 Google Ads n6 Supermetrics n4->n6 lands via n5 LinkedIn Ads n5->n6 lands via n6->n3 loads into n8 Looker n7->n8 provides attribution model n9 Marketing Team n8->n9 reads cost per pipeline
Hexagons are systems. Cylinders are data stores. Diamonds are decisions. Rounded orange boxes are people and teams. A dashed amber arrow is a link the AI inferred. Pink arrows are decision branches. Blue arrows move data.

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What is in this flow

  • Systems: Google Analytics 4, Google Ads, LinkedIn Ads, Supermetrics, dbt and Looker.
  • Data stores: BigQuery.
  • People and teams: Marketing Team.
  • Size: 9 steps and 8 connections.

Mermaid code

Copy this into any Markdown file, GitHub README, or Mermaid Live to render the same flow.

flowchart TD
  n1(["Website Visits"])
  n2{{"Google Analytics 4"}}
  n3[("BigQuery"])
  n4{{"Google Ads"}}
  n5{{"LinkedIn Ads"}}
  n6{{"Supermetrics"}}
  n7{{"dbt"}}
  n8{{"Looker"}}
  n9("Marketing Team")
  n1 -->|tracked in| n2
  n2 -->|exports raw events daily| n3
  n4 -->|lands via| n6
  n5 -->|lands via| n6
  n6 -->|loads into| n3
  n3 -->|joins events and spend| n7
  n7 -->|provides attribution model| n8
  n8 -->|reads cost per pipeline| n9

Map your own process

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