Core Concepts

Bruin is built around a few simple but powerful concepts that enable you to build, run, and manage data pipelines effectively. Understanding these concepts will help you make the most out of Bruin.

Overview

Concept	Description
Asset	Anything that carries value derived from data (tables, views, files, models)
Pipeline	A group of assets that are executed together in dependency order
Commands	CLI operations to run, validate, and manage your pipelines
Project	A Git repository containing your pipelines, configured via .bruin.yml
Variables	Dynamic values injected into your asset code during execution

Asset

An asset is anything that carries value derived from data:

• A table or view in your database
• A file in S3 or GCS
• A machine learning model
• A document in Excel, Google Sheets, or Notion

Assets consist of a definition (metadata) and content (the actual query or logic). Bruin supports multiple asset types including SQL, Python, R, and ingestr.

Learn more about Assets →

Pipeline

A pipeline is a collection of assets that execute together in the correct dependency order. Pipelines are defined using a pipeline.yml file and provide:

• Scheduling configuration
• Default connection settings
• Pipeline-level variables
• Notification settings

my-pipeline/
├── pipeline.yml
└── assets/
    ├── ingest_data.asset.yml
    ├── transform.sql
    └── export.py

Learn more about Pipelines →

Commands

Bruin provides a comprehensive CLI for managing your data pipelines. Commands can be executed in:

• Terminal: Direct CLI usage
• VS Code Extension: Visual interface with integrated features
• AI Agents: Via Bruin MCP for programmatic access

Key commands include:

Command	Description
run	Execute pipelines or individual assets
validate	Check pipeline configuration and syntax
init	Create new Bruin projects
lineage	Visualize asset dependencies

Learn more about Commands →

Project

A project is a Git repository that contains your data pipelines. The project is configured via the .bruin.yml file located at the root of your repository.

The .bruin.yml file contains:

• Connections: Built-in connection configurations for data platforms (BigQuery, Snowflake, etc.) and ingestion sources
• Secrets: Custom credentials and API keys that can be injected into your assets
• Environments: Configuration contexts that enable you to run the same pipeline code against different targets (local, staging, production)

Learn more about Project →

Variables

Variables are dynamic values provided during execution and injected into your asset code. There are two types:

• Built-in Variables: Automatically injected by Bruin (e.g., start_date, end_date, pipeline, run_id)
• Custom Variables: User-defined variables specified in pipeline.yml

Variables enable parameterized pipelines—for example, processing data for specific date ranges or customer segments without modifying code.

Learn more about Variables →