Data quality checks with dbt tests 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
Raw data lands in the Snowflake raw schema. dbt runs source freshness checks first. If a source is stale, the pipeline stops and posts to the data on-call Slack channel. If the sources are fresh, dbt builds the models and runs the tests. Failed tests open a ticket in Linear for the data engineer. Passing models are published to the analytics schema for Tableau.
The diagram
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What is in this flow
- Systems: dbt, Data On-Call Slack Channel, Linear and Tableau.
- Data stores: Snowflake Raw Schema and Analytics Schema.
- People and teams: Data Engineer.
- Decisions: Source Stale? and Tests Failed?.
- Size: 11 steps and 11 connections.
Questions to confirm
The AI inferred these links. On a real call, you ask the customer to confirm them.
- Does Data On-Call Slack Channel pipeline stops Pipeline Stop?
Mermaid code
Copy this into any Markdown file, GitHub README, or Mermaid Live to render the same flow.
flowchart TD
n1(["Raw Data Landing"])
n2[("Snowflake Raw Schema"])
n3{{"dbt"}}
n4{"Source Stale?"}
n5{{"Data On-Call Slack Channel"}}
n10(["Pipeline Stop"])
n11{"Tests Failed?"}
n6{{"Linear"}}
n7("Data Engineer")
n8[("Analytics Schema"])
n9{{"Tableau"}}
n1 -->|lands in| n2
n2 -->|runs source freshness checks| n3
n3 -->|checks freshness| n4
n4 -->|Yes| n5
n5 -.->|pipeline stops| n10
n4 -->|No| n3
n3 -->|runs tests| n11
n11 -->|Yes| n6
n6 -->|assigns ticket| n7
n11 -->|No| n8
n8 -->|published to| n9Map your own process
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