dbt-logger 0.1.1

A lightweight logging utility for dbt projects

dbt-logger

A logging utility that captures dbt command output and stores it in Snowflake for auditing and monitoring. Uses your existing dbt credentials - no additional configuration needed!

Installation

pip install dbt-logger

Quick Start

The simplest way - uses your existing dbt profiles.yml:

from dbt_logger import DbtLogger

# Automatically uses credentials from dbt profiles.yml
logger = DbtLogger(repo_name="my_analytics_project")

# Run dbt commands and log them
logger.run_command("dbt build")
logger.run_command("dbt run --select +sales_model")

That's it! Logs are automatically saved to [your_dbt_database].logging.dbt_logging

Configuration File (Optional)

Create dbt/dbt_logger.yml in your dbt project to configure logging destination:

# dbt/dbt_logger.yml

# Option 1: Custom schema in dbt database
schema: audit_logs

# Option 2: Completely separate database for logs
database: ADMIN_DB
schema: LOGGING
table_name: dbt_execution_logs

Precedence order:

1. Parameters passed to DbtLogger() (highest)
2. Settings in dbt/dbt_logger.yml
3. Defaults: {dbt_database}.logging.dbt_logging (lowest)

Configuration Options

Use Specific dbt Profile/Target

# Use a specific profile and target from profiles.yml
logger = DbtLogger(
    repo_name="my_project",
    profile_name="my_profile",  # Optional: defaults to first profile
    target="prod"                # Optional: defaults to profile's default target
)

Override Logging Location in Code

# Override config file settings
logger = DbtLogger(
    repo_name="my_project",
    database="ADMIN_DB",
    schema="LOGGING",
    table_name="my_custom_logs"
)

Manual Connection Parameters (Advanced)

If you need to use different credentials than dbt:

from dbt_logger import DbtLogger, get_connection_params_from_env

# Option 1: From environment variables
connection_params = get_connection_params_from_env()

# Option 2: Manual dict
connection_params = {
    'account': 'your_account',
    'user': 'your_user',
    'password': 'your_password',
    'database': 'your_database',
    'warehouse': 'your_warehouse',
}

logger = DbtLogger(
    repo_name="my_project",
    connection_params=connection_params
)

How It Works

1. Reads your dbt profiles.yml - Automatically finds and parses your dbt configuration
2. Supports private key auth - Works with your existing DBT_USER, DBT_PVK_PATH, DBT_PVK_PASS env vars
3. Creates logging infrastructure - Auto-creates schema and table on first run
4. Captures everything - Runs dbt commands and logs all output in real-time
5. Stores in Snowflake - Saves command history for auditing and monitoring

Logging Table Schema

-- Default location: [your_dbt_database].logging.dbt_logging
CREATE TABLE dbt_logging (
    repo_name STRING,      -- Your dbt project/repo name
    command STRING,        -- The dbt command executed
    stdout_log STRING,     -- Complete command output
    log_time TIMESTAMP_LTZ -- Execution timestamp
);

dbt profiles.yml Locations

The logger checks for profiles.yml in this order:

1. $DBT_PROFILES_DIR/profiles.yml
2. ./dbt/profiles/profiles.yml (current directory)
3. ~/.dbt/profiles.yml (default dbt location)

Features

• ✅ Zero Config - Uses your existing dbt credentials
• ✅ Private Key Auth - Full support for private key authentication
• ✅ Auto Setup - Creates schema and table automatically
• ✅ Real-time Output - See command output live while logging
• ✅ Flexible Storage - Default to dbt database or use separate logging database
• ✅ Environment Variables - Resolves Jinja env_var() expressions from profiles.yml

License

MIT License - see LICENSE file for details.