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Goal

With the rise of data-driven decision-making, having a robust pipeline is vital. The dbt-checkpoint is designed to help you maintain quality standards in your dbt projects, especially as they scale with more models, sources, and macros. In this guide, we’ll break down how to set up and use this powerful tool.

Understanding dbt-checkpoint

Think of dbt-checkpoint like a diligent quality inspector in a busy manufacturing plant. As the lines become more complex with numerous machines (models) doing their own tasks, it can be easy to overlook small defects (errors or omissions) that can lead to significant issues. dbt-checkpoint automates checks, ensuring everything meets the required standards, much like a quality inspector who verifies every production piece before it moves onto the next stage.

Telemetry Insights

dbt-checkpoint also has telemetry built in to improve its effectiveness. It collects information about which hooks you use often, helping its maintainers understand user preferences and improve the tool. Notably, it doesn’t track personal credentials or any sensitive information.

Installation Steps

To get started with dbt-checkpoint, follow these steps:

• Ensure you have pre-commit installed:

pip install pre-commit

• Create a file named `.pre-commit-config.yaml` in your project root and list the hooks you want to run before each commit. Example:

repos:
  - repo: https://github.com/dbt-checkpoint/dbt-checkpoint
    rev: v1.2.1
    hooks:
      - id: dbt-parse
      - id: check-model-has-all-columns

Running Checks in CI/CD

If you’re working within a CI/CD ecosystem, you can run dbt-checkpoint checks after you’ve pushed your changes to GitHub, although direct usage within dbt Cloud is not supported. The required `manifest.json` can be generated using `dbt-parse` command.

Creating a Workflow File

To set up your workflow:

1. Inside your GitHub repository, create a folder named `.github/workflows` if it doesn’t already exist.
2. Create a new workflow file (e.g., `pr.yml`) and define your workflow as follows:

name: dbt-checkpoint
on:
  push:
  pull_request:
    branches:
      - main
jobs:
  dbt-checkpoint:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v2
      - name: Run dbt checkpoint
        uses: dbt-checkpoint/action@v0.1

Troubleshooting

If you run into issues during installation or setup, here are a few tips:

• Ensure you are running Python 3.6 or above.
• Double-check your `.pre-commit-config.yaml` for syntax errors.
• If hooks are not triggering, try running pre-commit install again.
• If you have a suggestion for a new hook or find a bug, let us know.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.