Quickstart
Prerequisites
Before you begin, you’ll need:
- An existing dbt project with well-structured data models
- A Buster account (get started at buster.so)
Step 1: Install the CLI
Step 2: Authenticate with Buster
Before you can use Buster, you’ll need to authenticate with your Buster account:
This command will prompt you for an API key. You can find it in the Buster platform.
--local
flag to authenticate with your local Buster instance.Step 3: Initialize Your Project & Connect Data Source
Let’s initialize your Buster project and connect to your data source in one step using the buster init
command:
This interactive command:
- Asks you to select your data warehouse type (Postgres, BigQuery, Snowflake, etc.)
- Prompts for connection details like host, port, credentials, etc.
- Tests the connection to ensure everything works
- Detects dbt project configurations if present
- Recognizes your dbt project structure
- Finds model paths automatically
- Discovers your dbt catalog for semantic model generation
- Creates a
buster.yml
file with your project configuration:
The command handles both project initialization and data source onboarding in a single workflow, making setup much easier.
See our Data Sources guide for specific database connection instructions and our Init Command documentation for more details on the setup process.
Step 4: (Optional) Copy the AI Agent Documentation
This documentation provides comprehensive guidance for AI agents (like Claude, GPT, Cursor, etc.) when working with Buster’s semantic layer and configuration files. If you plan to use AI agents to help you build or manage your Buster project, you may find it useful to familiarize yourself with these guidelines.
You can find the AI Agent Documentation here: AI Agent Documentation.
Step 5: Create Semantic Models
The buster init
command (from Step 3) can generate your initial set of semantic models. However, if you skipped that part or need to generate base semantic models for new dbt models added after initialization, you can use the buster generate
command. This command analyzes your dbt project’s catalog to create the foundational semantic model files.
Buster analyzes your SQL models (like the examples below for models/orders.sql
and models/customers.sql
) and creates semantic model files for each one:
These semantic model files add business context to your SQL models. Buster will create a separate YAML file for each model (for example, models/customers.yml
shown below based on models/customers.sql
):
Step 6: Deploy Your Semantic Layer
Once you’ve created and configured your semantic models, deploy them to make them available for querying:
This command:
- Validates your semantic models
- Deploys them to your Buster instance
- Makes them immediately available for querying
Step 7: Chat with Your Data
Congrats! You can now chat with your data in the Buster. You can ask ad-hoc questions, generate reports and dashboards, and more.