dbt in CI/CD pipelines - Step-by-Step Execution

Concept Flow - dbt in CI/CD pipelines

Code Commit to Repo

↓

CI Pipeline Triggered

↓

Install dbt & Dependencies

↓

Run dbt Commands

↓

dbt run

↓

Report Results

↓

Deploy if OK

↓

Done

This flow shows how a code commit triggers a CI pipeline that installs dbt, runs dbt commands, reports results, and deploys if tests pass.

Execution Sample

dbt

git push origin main
# triggers CI pipeline
pip install dbt
dbt run
dbt test
dbt docs generate

This code simulates pushing changes that trigger a CI pipeline which installs dbt and runs dbt commands.

Execution Table

Step	Action	Command/Check	Result	Next Step
1	Code pushed to repo	git push origin main	Push accepted, CI triggered	Install dbt
2	Install dbt	pip install dbt	dbt installed successfully	Run dbt run
3	Run models	dbt run	Models compiled and run	Run dbt test
4	Run tests	dbt test	All tests passed	Generate docs
5	Generate docs	dbt docs generate	Documentation created	Report results
6	Report results	CI logs output	Success reported	Deploy if tests pass
7	Deploy	Deploy to production	Deployment done	Done
8	End	Pipeline complete	CI/CD pipeline finished successfully	Exit

💡 Pipeline ends after deployment and reporting success.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5	Final
dbt_installed	False	True	True	True	True	True
models_run	False	False	True	True	True	True
tests_passed	False	False	False	True	True	True
docs_generated	False	False	False	False	True	True
deployment_done	False	False	False	False	False	True

Key Moments - 3 Insights

Why do we run 'dbt test' after 'dbt run'?

Because 'dbt run' builds the models, and 'dbt test' checks if those models are correct. This order ensures tests run on the latest models, as shown in steps 3 and 4 in the execution_table.

What happens if tests fail during 'dbt test'?

The pipeline stops before deployment to avoid pushing bad changes. This is implied by the flow where deployment only happens after tests pass (step 4 to 7).

Why generate docs in the CI pipeline?

Generating docs (step 5) helps keep documentation updated automatically with the latest models, improving team communication and transparency.

Visual Quiz - 3 Questions

Look at the execution_table, what is the state of 'tests_passed' after step 4?

A False

B Unknown

C True

D Not applicable

💡 Hint

Check the variable_tracker row for 'tests_passed' after step 4.

At which step does the pipeline generate documentation?

A Step 5

B Step 4

C Step 3

D Step 6

💡 Hint

Look at the 'Action' column in the execution_table for 'Generate docs'.

If 'dbt test' fails, what happens next in the pipeline?

A Deployment proceeds anyway

B Pipeline stops before deployment

C Docs are generated anyway

D Tests are rerun automatically

💡 Hint

Refer to the key_moments explanation about test failure stopping deployment.

Concept Snapshot

dbt in CI/CD pipelines:
- Code push triggers CI pipeline
- Install dbt and dependencies
- Run 'dbt run' to build models
- Run 'dbt test' to validate models
- Generate docs with 'dbt docs generate'
- Deploy only if tests pass
- Automates safe, tested data model deployment

Full Transcript

When you push code to your repository, it triggers a CI pipeline. The pipeline first installs dbt and its dependencies. Then it runs 'dbt run' to build your data models. After that, it runs 'dbt test' to check if the models are correct. If all tests pass, it generates documentation using 'dbt docs generate'. Finally, if everything is successful, the pipeline deploys the changes to production. If tests fail, deployment stops to keep your data safe. This flow helps automate and secure your data transformations.

Practice

1. What is the main purpose of using dbt in a CI/CD pipeline?

easy

A. To create user interfaces for data visualization

B. To manually update data models in production

C. To automate building and testing data models before deployment

D. To store raw data without transformation

Solution

1. Step 1: Understand dbt's role in CI/CD

   dbt automates the building and testing of data models to ensure quality before deployment.

2. Step 2: Identify the main goal in CI/CD context

   The goal is to catch errors early by automating tests and builds, not manual updates or UI creation.

3. Final Answer:

   To automate building and testing data models before deployment -> Option C

4. Quick Check:

   dbt automates testing and building = C [OK]

Hint: Remember: CI/CD means automate build and test steps [OK]

Common Mistakes:

• Thinking dbt creates user interfaces
• Assuming dbt is for manual updates
• Confusing dbt with raw data storage

2. Which of the following is the correct command to run dbt models in a CI/CD pipeline?

easy

A. dbt build

B. dbt run

C. dbt deploy

D. dbt execute

Solution

1. Step 1: Recall dbt commands for running models

   The standard command to run models is dbt run.

2. Step 2: Eliminate incorrect commands

   dbt build combines multiple steps including run and test, but to specifically run models use dbt run. dbt deploy and dbt execute do not exist in dbt CLI.

3. Final Answer:

   dbt run -> Option B

4. Quick Check:

   Run models with 'dbt run' = A [OK]

Hint: Use 'dbt run' to execute models in CI/CD [OK]

Common Mistakes:

• Using 'dbt build' which is invalid
• Confusing 'dbt deploy' with deployment
• Trying 'dbt execute' which is not a command

3. Given this snippet in a CI/CD pipeline script:

dbt deps
 dbt seed
 dbt run
 dbt test

What is the expected output after running dbt test?

medium

A. It runs tests on the built models and reports any failures

B. It installs dependencies for dbt packages

C. It seeds the database with initial data

D. It compiles SQL files without running tests

Solution

1. Step 1: Understand the sequence of commands

   dbt deps installs dependencies, dbt seed loads seed data, dbt run builds models, and dbt test runs tests on those models.

2. Step 2: Identify what dbt test does

   dbt test checks the models for errors or data quality issues and reports failures.

3. Final Answer:

   It runs tests on the built models and reports any failures -> Option A

4. Quick Check:

   'dbt test' runs model tests = A [OK]

Hint: Remember: 'dbt test' checks model correctness after build [OK]

Common Mistakes:

• Confusing 'dbt test' with dependency installation
• Thinking 'dbt test' seeds data
• Assuming 'dbt test' only compiles SQL

4. You see this error in your CI/CD pipeline logs:

ERROR: No profiles.yml found

What is the most likely cause?

medium

A. The dbt profile configuration file is missing or not accessible

B. The dbt models have syntax errors

C. The database connection is slow

D. The seed data is empty

Solution

1. Step 1: Analyze the error message

   The error clearly states that profiles.yml is not found, which is the configuration file dbt uses to connect to the database.

2. Step 2: Understand the impact of missing profiles.yml

   Without this file, dbt cannot connect to the database, causing the pipeline to fail before running models or tests.

3. Final Answer:

   The dbt profile configuration file is missing or not accessible -> Option A

4. Quick Check:

   'No profiles.yml found' means missing config = D [OK]

Hint: Check for profiles.yml file in CI environment [OK]

Common Mistakes:

• Assuming syntax errors cause this message
• Blaming slow database connection
• Thinking empty seed data causes this error

5. In a CI/CD pipeline, you want to ensure that dbt seeds data only once before running models and tests. Which pipeline step order is best?

hard

A. dbt run -> dbt seed -> dbt test -> dbt deps

B. dbt seed -> dbt test -> dbt deps -> dbt run

C. dbt test -> dbt seed -> dbt run -> dbt deps

D. dbt deps -> dbt seed -> dbt run -> dbt test

Solution

1. Step 1: Understand the correct order of dbt commands

   First, dbt deps installs dependencies, then dbt seed loads seed data once, followed by dbt run to build models, and finally dbt test to validate models.

2. Step 2: Evaluate the options for logical sequence

   dbt deps -> dbt seed -> dbt run -> dbt test follows the correct logical order ensuring seed data is loaded before models run and tests execute last.

3. Final Answer:

   dbt deps -> dbt seed -> dbt run -> dbt test -> Option D

4. Quick Check:

   Seed before run and test = B [OK]

Hint: Seed data before running models and tests [OK]

Common Mistakes:

• Running models before seeding data
• Testing before building models
• Installing dependencies after running models