dbt Cloud deployment - Step-by-Step Execution

Concept Flow - dbt Cloud deployment

Write dbt models

↓

Commit code to Git

↓

Push to remote repository

↓

Trigger dbt Cloud job

↓

dbt Cloud runs models

↓

Check run results

↓

Deploy changes to warehouse

This flow shows how dbt Cloud deployment moves from writing models to running and deploying them in the data warehouse.

Execution Sample

dbt

models/my_model.sql
-- SQL select statement

-- Commit and push to Git

-- Trigger dbt Cloud job

-- Review job logs and results

This code snippet represents the steps from writing a dbt model to triggering a deployment job in dbt Cloud.

Execution Table

Step	Action	Input/Trigger	Process	Output/Result
1	Write dbt model	SQL file with select statement	Create model file in project	Model file ready for version control
2	Commit code	Model file changes	Git commit command	Changes saved locally
3	Push code	Local commits	Git push to remote repo	Code available in remote repository
4	Trigger job	Manual or scheduled trigger	dbt Cloud starts job	Job queued and running
5	Run models	dbt Cloud job	dbt compiles and runs SQL	Models built in warehouse
6	Check results	Job logs and status	Review success or errors	Deployment success or failure noted
7	Deploy changes	Successful run	Changes applied in warehouse	Data models updated and ready

💡 Process stops after successful deployment or failure logged for fixes

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	After Step 6	Final
Model File	None	Created	Created	Created	Created	Created	Created	Created in warehouse
Git Commit	None	None	Done	Done	Done	Done	Done	Done
Git Push	None	None	None	Done	Done	Done	Done	Done
dbt Job Status	Not started	Not started	Not started	Queued	Running	Completed	Completed	Completed
Deployment Status	Not deployed	Not deployed	Not deployed	Not deployed	Not deployed	Not deployed	Success/Failure	Success/Failure

Key Moments - 3 Insights

Why do we need to push code to a remote repository before triggering a dbt Cloud job?

Because dbt Cloud pulls the latest code from the remote repository to run the models. Without pushing, dbt Cloud won't see your changes (see execution_table step 3 and 4).

What happens if the dbt Cloud job fails during model run?

The job logs will show errors and the deployment status will be failure. You need to fix the errors and rerun the job (see execution_table step 6 and variable_tracker dbt Job Status).

Is the model immediately updated in the warehouse after writing the SQL file?

No, the model is only updated after the dbt Cloud job runs successfully and deploys changes (see variable_tracker Model File and Deployment Status).

Visual Quiz - 3 Questions

Look at the execution_table at step 4, what triggers the dbt Cloud job?

A Writing the SQL model file

B Manual or scheduled trigger

C Committing code locally

D Pushing code to remote repository

💡 Hint

Check the 'Input/Trigger' column in execution_table step 4

According to variable_tracker, what is the status of 'Git Push' after step 3?

A None

B Created

C Done

D Queued

💡 Hint

Look at the 'Git Push' row and 'After Step 3' column in variable_tracker

If the dbt Cloud job fails at step 6, what should you do next?

A Fix errors and rerun the job

B Write a new model file

C Push code again without changes

D Ignore and deploy anyway

💡 Hint

Refer to key_moments about job failure and execution_table step 6

Concept Snapshot

dbt Cloud deployment flow:
1. Write SQL models
2. Commit and push code to Git
3. Trigger dbt Cloud job
4. dbt runs models in warehouse
5. Check job results
6. Deploy changes if successful
Push code so dbt Cloud can access latest models.

Full Transcript

dbt Cloud deployment starts by writing SQL models. These models are saved as files. Next, you commit these changes locally using Git, then push them to a remote repository. dbt Cloud accesses this remote repo to get the latest code. You then trigger a dbt Cloud job manually or by schedule. The job runs your models by compiling and executing SQL in your data warehouse. After the run, you check the job logs for success or errors. If successful, your models are deployed and updated in the warehouse. If errors occur, you fix them and rerun the job. This process ensures your data models are version controlled and deployed safely.