What is the project about?

Bruin is a comprehensive data pipeline tool designed to streamline data ingestion, transformation, and quality assurance.

What problem does it solve?

It simplifies the creation and management of data pipelines by consolidating various stages (ingestion, transformation, quality checks) into a single framework, reducing complexity and improving efficiency. It solves the problem of needing multiple, disparate tools for different parts of the data pipeline.

What are the features of the project?

• Data ingestion using ingestr or Python.
• SQL and Python transformations across multiple data platforms.
• Table/view materializations, including incremental tables.
• Isolated Python environment management via uv.
• Built-in data quality checks.
• Jinja templating for DRY code.
• End-to-end pipeline validation through dry-runs.
• Flexible deployment: local machine, EC2, or GitHub Actions.
• Secure secrets injection using environment variables.
• VS Code extension for enhanced development.
• Written in Go for performance.
• Easy installation and usage.

What are the technologies used in the project?

• Go (Golang)
• SQL
• Python
• Jinja (templating)
• uv (Python environment management)
• ingestr (data ingestion)
• GitHub Actions (CI/CD)
• VS Code (IDE extension)

What are the benefits of the project?

• Unified Framework: Combines ingestion, transformation, and quality checks.
• Platform Agnostic: Supports various data platforms.
• Flexibility: Runs locally, on servers, or in CI/CD.
• Developer-Friendly: VS Code extension, templating, and dry-run features.
• Performance: Built with Go for speed and efficiency.
• Simplified Data Pipelines: Reduces the complexity of building and managing data workflows.
• Improved Data Quality: Integrated data quality checks.
• Code Reusability: Jinja templating minimizes code duplication.

What are the use cases of the project?

• Building and managing ETL/ELT pipelines.
• Data warehousing and data lake construction.
• Data migration between different platforms.
• Automating data transformations and quality checks.
• Creating reproducible data pipelines for analytics and reporting.
• Any scenario requiring the movement, transformation, and validation of data.