Lesson 3 of 12

Structured learning draft

Constraints with dbt

In Data Warehousing & BigQuery, the way a learner handles constraints shapes how dbt is used and evaluated. A constraint prevents states that violate a declared rule. This intermediate lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain constraints in the context of Data Warehousing & BigQuery.
Apply dbt to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureNested structure

Constraints: from context to evidence

Constraints connects business rule and records to a valid data state in Data Warehousing & BigQuery.

Business rule and records

Define the purpose, intended user and dbt constraints.

frames

Constraints

Place invariants near data and test rejected writes.

produces evidence for

Valid data state

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Constraints is credible only when the result can be traced back to its purpose, inputs and constraints. Constraints is the decision layer between the starting context and evidence that the result is fit for purpose.

A constraint prevents states that violate a declared rule. For dbt, distinguish performing an operation from demonstrating that it suits the stated purpose. Place invariants near data and test rejected writes. Record assumptions that could change the conclusion.

Apply constraints deliberately

State the Data Warehousing & BigQuery task and the decision it supports.
Prepare a small dbt case with a known input and difficult boundary.
Place invariants near data and test rejected writes.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

constraints evidence record
Review point	Evidence
Purpose	The specific dbt outcome and intended user
Method	The constraints decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using dbt before defining what constraints must achieve.
Checking only the easiest Data Warehousing & BigQuery example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For Data Warehousing & BigQuery, complete a bounded dbt task demonstrating constraints. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In Data Warehousing & BigQuery, which evidence best supports a constraints result produced with dbt?

A polished dbt output without its input A screenshot showing the tool opened The input, expected behaviour, normal and boundary results, and a limitation A claim that the method always works

Lesson summary

For Data Warehousing & BigQuery, constraints means: A constraint prevents states that violate a declared rule.
A credible dbt result includes a checked boundary, not only a successful example.
The next lesson builds on this constraints evidence record.

Sources and further reading

PostgreSQL TutorialPostgreSQL Global Development Group - accessed 2026-08-21
SQL LanguagePostgreSQL Global Development Group - accessed 2026-08-21

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