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How to Set up Git Pre-Commit Hooks for a DataOps Project

Setting up pre-commit hooks for a project is essential in ensuring code and data quality throughout the development lifecycle. Pre-commit hooks are scripts or commands that run automatically before a commit to a version control system, such as Git. These hooks can check the code or data being committed for syntax errors, style violations, or other issues and prevent the commit from being made if any issues are found.

To set up pre-commit hooks for a project:

  1. Define the hooks you want to use.

    You can write these hooks in any language and perform any checks necessary for your project. For example, you might use a Python script to check the syntax and formatting of Python code or a SQL script to check the quality of your database queries.

  2. Add the pre-commit hooks to your project's repository by creating a pre-commit configuration file at the project's root.

    This configuration file specifies the hooks to run and the order in which they should be run. You can also specify any options or arguments that the hooks should use. Alternatively, you can use hooks created by third-party vendors listed on the pre-commit documentation page.

    Here is an example configuration that shows both use cases:

    # .pre-commit-config.yaml
    - repo:
    rev: v4.4.0 # this is optional, use `pre-commit autoupdate` to get the latest rev!
    - id: check-yaml
    - id: end-of-file-fixer
    - id: trailing-whitespace
    - repo:
    rev: v1.4.0
    - id: detect-secrets
    - repo: local
    - id: custom-hook
    language: python
    name: custom-hook
    entry: hooks/
  3. Install the pre-commit tool on your local machine by running the below command in the local copy of the repository:

    pre-commit install and pre-commit autoupdate

    This tool automatically runs the pre-commit hooks before each commit and prevents the commit from being made if any issues are found.

Setting up pre-commit hooks for a project is crucial in ensuring the quality and consistency of your code and data. By automating checks and preventing issues from being committed, you can save time and reduce the risk of errors or bugs in your project.