Manual onboarding is necessary if you do not have a dbt project. In this case, you will need to build your Buster project manually. This means you won’t be able to use buster init, buster generate, or any command that relies on dbt metadata.

To onboard manually, follow these steps:

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Manual Onboarding Steps

  1. Connect your data source in the Buster UI: You can do this at https://platform.buster.so/app/settings/datasources/add. For more detailed instructions on connecting data sources, please refer to our Data Sources Overview.

  2. Build your buster.yml file: After connecting your data source, you’ll need to create and configure your buster.yml file.

  3. Organize your project folders: It’s recommended to create a folder for each database or schema. Each of these will be configured as a separate project for Buster to reference in the buster.yml file.

  4. Create your Semantic Models: Within each project folder, you will need to create your Semantic Models. These models should align with the tables in your database or data warehouse.

  5. Deploy your models: Once your data source is connected and your buster.yml and semantic models are configured, you can parse and deploy your project. Use the following commands:

    buster parse
buster deploy

Even with manual onboarding, you can still utilize the buster parse and buster deploy commands.

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Example Project Structure

Here is an example of how you might structure your project:

your-buster-project/
├── buster.yml
├── marketing/                 # Folder for the 'marketing_analytics' schema
│   └── semantic_models/
│       ├── customers.yml
│       └── campaigns.yml
├── finance/                   # Folder for the 'finance' schema
│   └── semantic_models/
│       ├── transactions.yml
│       └── accounts.yml
└── sales/                     # Folder for the 'sales_data' database (if it's a separate data source)
    └── semantic_models/
        ├── orders.yml
        └── products.yml

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Example buster.yml

This buster.yml file defines three projects, corresponding to the folders in the example structure above.

projects:
  - path: ./marketing
    data_source_name: your_data_warehouse # e.g., snowflake_prod, bigquery_main
    schema: marketing_analytics
    database: main_db # or the relevant database for this schema
    semantic_model_paths:
      - semantic_models/
  
  - path: ./finance
    data_source_name: your_data_warehouse 
    schema: finance
    database: main_db
    semantic_model_paths:
      - semantic_models/

  - path: ./sales # Assuming 'sales' is a different database or requires separate handling
    data_source_name: sales_specific_source # Could be the same or different from above
    database: sales_data
    schema: public # Or the relevant schema for the sales_data database
    semantic_model_paths:
      - semantic_models/

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Example Semantic Models

Here are a couple of simplified semantic model examples that would reside in the respective semantic_models folders.

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marketing/semantic_models/customers.yml

name: customers
description: Model representing customer data from the marketing analytics schema.
# data_source_name, database, and schema will be inherited from buster.yml
dimensions:
  - name: customer_id
    description: Unique identifier for the customer
    type: VARCHAR
  - name: email
    description: Customer's email address
    type: VARCHAR
    searchable: true
  - name: acquisition_date
    description: Date the customer was acquired
    type: DATE
measures:
  - name: total_spent
    description: Total amount spent by the customer
    type: DECIMAL # Assuming this comes from a related orders/transactions table
relationships:
  - name: campaigns
    source_col: customer_id
    ref_col: customer_id # Assuming campaigns model has a customer_id
    description: Links to marketing campaigns the customer was part of.

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finance/semantic_models/transactions.yml

name: transactions
description: Model for financial transactions.
# data_source_name, database, and schema will be inherited from buster.yml
dimensions:
  - name: transaction_id
    description: Unique identifier for the transaction
    type: INTEGER
  - name: transaction_date
    description: Date of the transaction
    type: TIMESTAMP
  - name: account_id
    description: Identifier for the associated account
    type: VARCHAR
measures:
  - name: transaction_amount
    description: The amount of the transaction
    type: DECIMAL
metrics:
  - name: average_transaction_value
    expr: "SUM(transaction_amount) / COUNT(transaction_id)"
    description: Average value per transaction
relationships:
  - name: accounts
    source_col: account_id
    ref_col: account_id # Assuming accounts model has an account_id
    description: Links to the accounts model.